Vibecode LanguageTool
track this build5 phases, 16 steps, beginner friendly0%YES · one-shottable
Because the core engine is open source, a technical user can run the grammar checker locally or self-host the server for many use cases.
the project pack5 files · written for this build, step by step
You are building a lean indie version of LanguageTool. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # LanguageTool · indie build Build a local grammar-checking client around the existing LanguageTool engine. Estimated effort: **multi-day**. Work `BUILD_PLAN.md` top to bottom · every phase ends in a check that has to pass before the next one starts. ## Stack | Part | Choice | Why | | --- | --- | --- | | Runtime | Python 3.12+ and Flask | A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. | | Storage | SQLite through Python sqlite3 | Local ownership with explicit transactions and a portable backup path. | | Interface | Jinja HTML templates, CSS and vanilla JavaScript | Implement the specified screens directly with labelled controls and progressive enhancement. | | Verification | Python unittest and browser acceptance fixtures | Check invariants and the actual user workflow before the next phase. | | Grammar | LanguageTool Java HTTP server | Reuse existing local language rules rather than writing a grammar engine. | ## Before you start Have every one of these ready. The plan assumes them from step one. - [ ] **Python 3.12 or newer and a terminal** · Python is free; this local prototype requires no hosting subscription. - Why: The local backend, tests and storage use Python. - Get it: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. - Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. - [ ] **A current desktop browser and text editor** · No paid service required for this local step. - Why: You need to view the local interface and edit generated project files. - Get it: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. - Verify: You can create a text file in the project folder and open browser developer tools. - [ ] **LanguageTool acceptance sample** · No paid service required for this local step. - Why: This fixture makes the first end-to-end check reproducible. - Get it: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. - Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. - [ ] **Confirm the bounded replacement** · No paid service required for this local step. - Why: The full commercial product is larger than this guide. - Get it: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. - Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. - [ ] **LanguageTool Java HTTP server** · Local basic engine; premium cloud-only rules are outside this build. - Why: The app reuses the existing grammar engine. - Get it: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. - Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. ## Quick start ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` Then copy `.env.example` to `.env` and fill in the values it documents. ## Honest limits This build deliberately does not replace: - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support If one of those is essential to you, that is the reason to keep paying for LanguageTool, and the README should say so rather than pretend. ===== BRIEF.md ===== # Build brief · LanguageTool The one-shot brief this plan expands. `BUILD_PLAN.md` (or `MILESTONES.md`) is the same sequence broken into steps and checks; where the two disagree, the plan wins. Build me a local grammar-checking client around the existing LanguageTool engine as a bounded replacement for LanguageTool. Build it in phases in the order below. Do not write the whole product in one pass. Finish the current phase, run its Done when checks, fix failures, then continue. Indie mode completes Phases 1-5. Phase 6 is required before shared or public deployment. ### Stack (fixed, do not substitute) - Runtime: Python 3.12+ and Flask. A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. - Storage: SQLite through Python sqlite3. Local ownership with explicit transactions and a portable backup path. - Interface: Jinja HTML templates, CSS and vanilla JavaScript. Implement the specified screens directly with labelled controls and progressive enhancement. - Verification: Python unittest and browser acceptance fixtures. Check invariants and the actual user workflow before the next phase. - Grammar: LanguageTool Java HTTP server. Reuse existing local language rules rather than writing a grammar engine. ### Data model (create this before Phase 1) - documents(id,path,text,revision) - results(document_id,revision,rule_id,offset,length,message,replacements_json) - ignored_rules(rule_id) - dictionary(word) - profiles(id,language,disabled_rules_json) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. ### Prerequisites and configuration - Python 3.12 or newer and a terminal: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. Cost: Python is free; this local prototype requires no hosting subscription. - A current desktop browser and text editor: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. Verify: You can create a text file in the project folder and open browser developer tools. Cost: No paid service required for this local step. - LanguageTool acceptance sample: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. Cost: No paid service required for this local step. - Confirm the bounded replacement: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. Cost: No paid service required for this local step. - LanguageTool Java HTTP server: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. Cost: Local basic engine; premium cloud-only rules are outside this build. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_URL, LANGUAGETOOL_URL. Ship .env.example, replace placeholders and exclude secrets from git. ### Phase 1 · Local foundation and data contract Build: Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. - Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Commands, after creating the named files: `python -m venv .venv`; `python -m pip install Flask Flask-WTF python-dotenv waitress tzdata`; `python -m pip freeze > requirements.txt`; `python -m flask --app app run --host 127.0.0.1`. - For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Commands, after creating the named files: `python manage.py init`; `python manage.py seed`. - Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. - In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Commands, after creating the named files: `java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081`. - Trap: The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. Done when: The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. Restarting preserves the seeded records or canonical files; running init again deletes nothing. A cross-origin state-changing request to an admin route is refused. Do not build yet: Do not start deploy the grammar engine until these checks pass. ### Phase 2 · Deploy the grammar engine Build: Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: A local installation does not include all premium cloud rules or synonyms. Done when: The probe returns structured matches for the intentionally incorrect sample. A stopped engine yields a clear unavailable status instead of an empty success result. Do not build yet: Do not start review suggestions until these checks pass. ### Phase 3 · Review suggestions Build: Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Do not blindly apply all offsets after the first replacement has changed text length. Done when: The protected noun disappears from displayed issues while other errors remain. Accepting one suggestion triggers a fresh check against the revised text. Do not build yet: Do not start file workflow until these checks pass. ### Phase 4 · File workflow Build: File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Browser extensions and every-language quality parity are beyond this local engine client. Done when: The fixture CLI exits nonzero and identifies the relevant source line. An engine timeout cannot be mistaken for a clean document in CI. Do not build yet: Do not start portable delivery and clean-start proof until these checks pass. ### Phase 5 · Portable delivery and clean-start proof Build: Finish the LanguageTool replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. - Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. - Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Commands, after creating the named files: `python -m pip install -r requirements.txt`; `python -m unittest discover -s test`. - Trap: Export without an import or restore rehearsal is not verified portability. Done when: A new folder reaches the documented fixture outcome using only README instructions. A backup restored in isolation preserves the proof data and can be opened. Do not build yet: Do not start operate and share with access controls until these checks pass. ### Phase 6 · Operate and share with access controls (product-builder only) Build: Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. - Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. - Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Commands, after creating the named files: `waitress-serve --listen=127.0.0.1:5000 app:app`. - Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. - Trap: Product mode is additional engineering, not a switch that makes a local prototype production-ready. Done when: An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. Do not build yet: Do not add new providers or vendor-scale features until the release gates are recorded. ### Out of scope (and why) - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set: retain the paid product if this capability is essential; it is outside this bounded build. - hosted server convenience: retain the paid product if this capability is essential; it is outside this bounded build. - browser extension premium flows: retain the paid product if this capability is essential; it is outside this bounded build. - team/admin options: retain the paid product if this capability is essential; it is outside this bounded build. - support: retain the paid product if this capability is essential; it is outside this bounded build. ### README must contain - Exact install, configure, initialize, start, test and recovery commands for a clean folder. - The schema, fixture, acceptance results, private data locations and export format. - Every prerequisite and secret source, actual running costs and deliberately excluded capabilities. - An honest distinction between tests actually run and checks still awaiting real credentials or deployment. - Dependency documentation reviewed 2026-09-05: https://flask.palletsprojects.com/en/stable/installation/ ; https://docs.python.org/3/library/sqlite3.html ; https://flask-wtf.readthedocs.io/en/1.2.x/csrf/ ; https://docs.pylonsproject.org/projects/waitress/en/stable/usage.html ; https://dev.languagetool.org/http-server. Recheck before upgrading; freeze the versions actually tested. ===== AGENTS.md ===== # Agent instructions · LanguageTool indie build - Read `README.md` and `BUILD_PLAN.md` before writing code. The stack is fixed: Python 3.12+ and Flask, SQLite through Python sqlite3, Jinja HTML templates, CSS and vanilla JavaScript, Python unittest and browser acceptance fixtures, LanguageTool Java HTTP server. Do not substitute. - Work one phase at a time, in order. Do not start a phase until every "Done when" item of the previous one passes. - Prefer the fewest moving parts that satisfy the step. No frameworks, services or dependencies the plan does not name. - Secrets live in `.env`, never in source or logs. Keep `.env.example` current when a variable is introduced. - Do not invent cryptography, security guarantees, APIs or compliance claims. - Add a focused test for every destructive, security-sensitive or data-loss path the plan names. - Run the project checks before declaring a phase complete, and record any deliberate shortcut in the README under "Tradeoffs". ## Known traps - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. - A local installation does not include all premium cloud rules or synonyms. - Do not blindly apply all offsets after the first replacement has changed text length. - Browser extensions and every-language quality parity are beyond this local engine client. - Export without an import or restore rehearsal is not verified portability. ===== BUILD_PLAN.md ===== # Build plan · LanguageTool Build a local grammar-checking client around the existing LanguageTool engine. Phases are in dependency order. Each ends in a "Done when" list; treat an unticked item as a blocker, not a note. ## Phase 1 · Local foundation and data contract Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. ### Steps 1. Create and start the local application Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Files: `app.py`, `requirements.txt`, `.env.example`, `.gitignore` ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` 2. Implement storage and seed the named fixture For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Files: `schema.sql`, `db.py`, `manage.py`, `fixtures/README.md` ```sh python manage.py init python manage.py seed ``` 3. Protect the local request boundary Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. Files: `app.py`, `templates/base.html`, `test/test_foundation.py` 4. Start the grammar service In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Files: `providers/grammar.py` ```sh java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081 ``` ### Done when - [ ] The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. - [ ] Restarting preserves the seeded records or canonical files; running init again deletes nothing. - [ ] A cross-origin state-changing request to an admin route is refused. ### Watch out - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. ## Phase 2 · Deploy the grammar engine Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. ### Steps 1. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Files: `features/deploy_the_grammar_engine.py`, `templates/deploy_the_grammar_engine.html` 2. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Files: `features/deploy_the_grammar_engine.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_deploy_the_grammar_engine.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] A stopped engine yields a clear unavailable status instead of an empty success result. ### Watch out - A local installation does not include all premium cloud rules or synonyms. ## Phase 3 · Review suggestions Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. ### Steps 1. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Files: `features/review_suggestions.py`, `templates/review_suggestions.html` 2. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Files: `features/review_suggestions.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_review_suggestions.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] Accepting one suggestion triggers a fresh check against the revised text. ### Watch out - Do not blindly apply all offsets after the first replacement has changed text length. ## Phase 4 · File workflow File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. ### Steps 1. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Files: `features/file_workflow.py`, `templates/file_workflow.html` 2. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Files: `features/file_workflow.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_file_workflow.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] An engine timeout cannot be mistaken for a clean document in CI. ### Watch out - Browser extensions and every-language quality parity are beyond this local engine client. ## Phase 5 · Portable delivery and clean-start proof Finish the LanguageTool replacement with a documented first-run path and a recoverable export. ### Steps 1. Write the user-facing runbook Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. Files: `README.md`, `.env.example` 2. Create and rehearse a recoverable backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Files: `OPERATIONS.md`, `scripts/backup.py` 3. Reproduce the first-run experience Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Files: `README.md`, `verification.md` ```sh python -m pip install -r requirements.txt python -m unittest discover -s test ``` ### Done when - [ ] A new folder reaches the documented fixture outcome using only README instructions. - [ ] A backup restored in isolation preserves the proof data and can be opened. ### Watch out - Export without an import or restore rehearsal is not verified portability. ## Not in this build - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support ## After v1, if you want it - Extend file workflow only after the fixture and recovery checks pass. - Before adding another integration, document its credentials, limits, cost and failure behavior with primary-source links. ===== .env.example ===== # Copy to .env and fill in. Never commit .env; this file documents it. # Required. A new private folder this application creates on first run; never serve it as static files. DATA_DIR=./data/languagetool # Required · secret. Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. SECRET_KEY=replace-with-a-random-secret # Required. Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. APP_MODE=indie # Required. The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. SITE_URL=http://127.0.0.1:5000 # Required. The loopback Java server started from the official distribution; Python proxies checks to it. LANGUAGETOOL_URL=http://127.0.0.1:8081
You are building a lean indie version of LanguageTool. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # LanguageTool · indie build Build a local grammar-checking client around the existing LanguageTool engine. Estimated effort: **multi-day**. Work `BUILD_PLAN.md` top to bottom · every phase ends in a check that has to pass before the next one starts. ## Stack | Part | Choice | Why | | --- | --- | --- | | Runtime | Python 3.12+ and Flask | A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. | | Storage | SQLite through Python sqlite3 | Local ownership with explicit transactions and a portable backup path. | | Interface | Jinja HTML templates, CSS and vanilla JavaScript | Implement the specified screens directly with labelled controls and progressive enhancement. | | Verification | Python unittest and browser acceptance fixtures | Check invariants and the actual user workflow before the next phase. | | Grammar | LanguageTool Java HTTP server | Reuse existing local language rules rather than writing a grammar engine. | ## Before you start Have every one of these ready. The plan assumes them from step one. - [ ] **Python 3.12 or newer and a terminal** · Python is free; this local prototype requires no hosting subscription. - Why: The local backend, tests and storage use Python. - Get it: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. - Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. - [ ] **A current desktop browser and text editor** · No paid service required for this local step. - Why: You need to view the local interface and edit generated project files. - Get it: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. - Verify: You can create a text file in the project folder and open browser developer tools. - [ ] **LanguageTool acceptance sample** · No paid service required for this local step. - Why: This fixture makes the first end-to-end check reproducible. - Get it: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. - Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. - [ ] **Confirm the bounded replacement** · No paid service required for this local step. - Why: The full commercial product is larger than this guide. - Get it: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. - Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. - [ ] **LanguageTool Java HTTP server** · Local basic engine; premium cloud-only rules are outside this build. - Why: The app reuses the existing grammar engine. - Get it: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. - Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. ## Quick start ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` Then copy `.env.example` to `.env` and fill in the values it documents. ## Honest limits This build deliberately does not replace: - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support If one of those is essential to you, that is the reason to keep paying for LanguageTool, and the README should say so rather than pretend. ===== BRIEF.md ===== # Build brief · LanguageTool The one-shot brief this plan expands. `BUILD_PLAN.md` (or `MILESTONES.md`) is the same sequence broken into steps and checks; where the two disagree, the plan wins. Build me a local grammar-checking client around the existing LanguageTool engine as a bounded replacement for LanguageTool. Build it in phases in the order below. Do not write the whole product in one pass. Finish the current phase, run its Done when checks, fix failures, then continue. Indie mode completes Phases 1-5. Phase 6 is required before shared or public deployment. ### Stack (fixed, do not substitute) - Runtime: Python 3.12+ and Flask. A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. - Storage: SQLite through Python sqlite3. Local ownership with explicit transactions and a portable backup path. - Interface: Jinja HTML templates, CSS and vanilla JavaScript. Implement the specified screens directly with labelled controls and progressive enhancement. - Verification: Python unittest and browser acceptance fixtures. Check invariants and the actual user workflow before the next phase. - Grammar: LanguageTool Java HTTP server. Reuse existing local language rules rather than writing a grammar engine. ### Data model (create this before Phase 1) - documents(id,path,text,revision) - results(document_id,revision,rule_id,offset,length,message,replacements_json) - ignored_rules(rule_id) - dictionary(word) - profiles(id,language,disabled_rules_json) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. ### Prerequisites and configuration - Python 3.12 or newer and a terminal: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. Cost: Python is free; this local prototype requires no hosting subscription. - A current desktop browser and text editor: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. Verify: You can create a text file in the project folder and open browser developer tools. Cost: No paid service required for this local step. - LanguageTool acceptance sample: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. Cost: No paid service required for this local step. - Confirm the bounded replacement: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. Cost: No paid service required for this local step. - LanguageTool Java HTTP server: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. Cost: Local basic engine; premium cloud-only rules are outside this build. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_URL, LANGUAGETOOL_URL. Ship .env.example, replace placeholders and exclude secrets from git. ### Phase 1 · Local foundation and data contract Build: Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. - Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Commands, after creating the named files: `python -m venv .venv`; `python -m pip install Flask Flask-WTF python-dotenv waitress tzdata`; `python -m pip freeze > requirements.txt`; `python -m flask --app app run --host 127.0.0.1`. - For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Commands, after creating the named files: `python manage.py init`; `python manage.py seed`. - Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. - In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Commands, after creating the named files: `java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081`. - Trap: The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. Done when: The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. Restarting preserves the seeded records or canonical files; running init again deletes nothing. A cross-origin state-changing request to an admin route is refused. Do not build yet: Do not start deploy the grammar engine until these checks pass. ### Phase 2 · Deploy the grammar engine Build: Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: A local installation does not include all premium cloud rules or synonyms. Done when: The probe returns structured matches for the intentionally incorrect sample. A stopped engine yields a clear unavailable status instead of an empty success result. Do not build yet: Do not start review suggestions until these checks pass. ### Phase 3 · Review suggestions Build: Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Do not blindly apply all offsets after the first replacement has changed text length. Done when: The protected noun disappears from displayed issues while other errors remain. Accepting one suggestion triggers a fresh check against the revised text. Do not build yet: Do not start file workflow until these checks pass. ### Phase 4 · File workflow Build: File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Browser extensions and every-language quality parity are beyond this local engine client. Done when: The fixture CLI exits nonzero and identifies the relevant source line. An engine timeout cannot be mistaken for a clean document in CI. Do not build yet: Do not start portable delivery and clean-start proof until these checks pass. ### Phase 5 · Portable delivery and clean-start proof Build: Finish the LanguageTool replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. - Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. - Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Commands, after creating the named files: `python -m pip install -r requirements.txt`; `python -m unittest discover -s test`. - Trap: Export without an import or restore rehearsal is not verified portability. Done when: A new folder reaches the documented fixture outcome using only README instructions. A backup restored in isolation preserves the proof data and can be opened. Do not build yet: Do not start operate and share with access controls until these checks pass. ### Phase 6 · Operate and share with access controls (product-builder only) Build: Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. - Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. - Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Commands, after creating the named files: `waitress-serve --listen=127.0.0.1:5000 app:app`. - Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. - Trap: Product mode is additional engineering, not a switch that makes a local prototype production-ready. Done when: An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. Do not build yet: Do not add new providers or vendor-scale features until the release gates are recorded. ### Out of scope (and why) - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set: retain the paid product if this capability is essential; it is outside this bounded build. - hosted server convenience: retain the paid product if this capability is essential; it is outside this bounded build. - browser extension premium flows: retain the paid product if this capability is essential; it is outside this bounded build. - team/admin options: retain the paid product if this capability is essential; it is outside this bounded build. - support: retain the paid product if this capability is essential; it is outside this bounded build. ### README must contain - Exact install, configure, initialize, start, test and recovery commands for a clean folder. - The schema, fixture, acceptance results, private data locations and export format. - Every prerequisite and secret source, actual running costs and deliberately excluded capabilities. - An honest distinction between tests actually run and checks still awaiting real credentials or deployment. - Dependency documentation reviewed 2026-09-05: https://flask.palletsprojects.com/en/stable/installation/ ; https://docs.python.org/3/library/sqlite3.html ; https://flask-wtf.readthedocs.io/en/1.2.x/csrf/ ; https://docs.pylonsproject.org/projects/waitress/en/stable/usage.html ; https://dev.languagetool.org/http-server. Recheck before upgrading; freeze the versions actually tested. ===== AGENTS.md ===== # Agent instructions · LanguageTool indie build - Read `README.md` and `BUILD_PLAN.md` before writing code. The stack is fixed: Python 3.12+ and Flask, SQLite through Python sqlite3, Jinja HTML templates, CSS and vanilla JavaScript, Python unittest and browser acceptance fixtures, LanguageTool Java HTTP server. Do not substitute. - Work one phase at a time, in order. Do not start a phase until every "Done when" item of the previous one passes. - Prefer the fewest moving parts that satisfy the step. No frameworks, services or dependencies the plan does not name. - Secrets live in `.env`, never in source or logs. Keep `.env.example` current when a variable is introduced. - Do not invent cryptography, security guarantees, APIs or compliance claims. - Add a focused test for every destructive, security-sensitive or data-loss path the plan names. - Run the project checks before declaring a phase complete, and record any deliberate shortcut in the README under "Tradeoffs". ## Known traps - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. - A local installation does not include all premium cloud rules or synonyms. - Do not blindly apply all offsets after the first replacement has changed text length. - Browser extensions and every-language quality parity are beyond this local engine client. - Export without an import or restore rehearsal is not verified portability. ===== BUILD_PLAN.md ===== # Build plan · LanguageTool Build a local grammar-checking client around the existing LanguageTool engine. Phases are in dependency order. Each ends in a "Done when" list; treat an unticked item as a blocker, not a note. ## Phase 1 · Local foundation and data contract Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. ### Steps 1. Create and start the local application Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Files: `app.py`, `requirements.txt`, `.env.example`, `.gitignore` ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` 2. Implement storage and seed the named fixture For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Files: `schema.sql`, `db.py`, `manage.py`, `fixtures/README.md` ```sh python manage.py init python manage.py seed ``` 3. Protect the local request boundary Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. Files: `app.py`, `templates/base.html`, `test/test_foundation.py` 4. Start the grammar service In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Files: `providers/grammar.py` ```sh java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081 ``` ### Done when - [ ] The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. - [ ] Restarting preserves the seeded records or canonical files; running init again deletes nothing. - [ ] A cross-origin state-changing request to an admin route is refused. ### Watch out - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. ## Phase 2 · Deploy the grammar engine Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. ### Steps 1. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Files: `features/deploy_the_grammar_engine.py`, `templates/deploy_the_grammar_engine.html` 2. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Files: `features/deploy_the_grammar_engine.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_deploy_the_grammar_engine.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] A stopped engine yields a clear unavailable status instead of an empty success result. ### Watch out - A local installation does not include all premium cloud rules or synonyms. ## Phase 3 · Review suggestions Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. ### Steps 1. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Files: `features/review_suggestions.py`, `templates/review_suggestions.html` 2. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Files: `features/review_suggestions.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_review_suggestions.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] Accepting one suggestion triggers a fresh check against the revised text. ### Watch out - Do not blindly apply all offsets after the first replacement has changed text length. ## Phase 4 · File workflow File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. ### Steps 1. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Files: `features/file_workflow.py`, `templates/file_workflow.html` 2. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Files: `features/file_workflow.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_file_workflow.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] An engine timeout cannot be mistaken for a clean document in CI. ### Watch out - Browser extensions and every-language quality parity are beyond this local engine client. ## Phase 5 · Portable delivery and clean-start proof Finish the LanguageTool replacement with a documented first-run path and a recoverable export. ### Steps 1. Write the user-facing runbook Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. Files: `README.md`, `.env.example` 2. Create and rehearse a recoverable backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Files: `OPERATIONS.md`, `scripts/backup.py` 3. Reproduce the first-run experience Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Files: `README.md`, `verification.md` ```sh python -m pip install -r requirements.txt python -m unittest discover -s test ``` ### Done when - [ ] A new folder reaches the documented fixture outcome using only README instructions. - [ ] A backup restored in isolation preserves the proof data and can be opened. ### Watch out - Export without an import or restore rehearsal is not verified portability. ## Not in this build - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support ## After v1, if you want it - Extend file workflow only after the fixture and recovery checks pass. - Before adding another integration, document its credentials, limits, cost and failure behavior with primary-source links. ===== .env.example ===== # Copy to .env and fill in. Never commit .env; this file documents it. # Required. A new private folder this application creates on first run; never serve it as static files. DATA_DIR=./data/languagetool # Required · secret. Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. SECRET_KEY=replace-with-a-random-secret # Required. Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. APP_MODE=indie # Required. The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. SITE_URL=http://127.0.0.1:5000 # Required. The loopback Java server started from the official distribution; Python proxies checks to it. LANGUAGETOOL_URL=http://127.0.0.1:8081
You are building a production product version of LanguageTool. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # LanguageTool · product brief ## Problem Because the core engine is open source, a technical user can run the grammar checker locally or self-host the server for many use cases. ## Product outcome An operated version of a local grammar-checking client around the existing LanguageTool engine, with authorized access, restore evidence and honest scope limits. ## Target user A builder who needs a maintainable product foundation, not a one-off demo. ## Required capabilities - Java runtime or Docker - local/server install - browser/editor integration - optional LLM API ## Explicit non-goals for v1 - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support ## Success criteria - The probe returns structured matches for the intentionally incorrect sample. - The protected noun disappears from displayed issues while other errors remain. - The fixture CLI exits nonzero and identifies the relevant source line. - A private-data authorization check passes using a second identity. - An off-host restore drill has a date, observed result and recovery time in OPERATIONS.md. - README names the excluded vendor capabilities and actual provider/hosting costs. ===== BRIEF.md ===== # Build brief · LanguageTool The one-shot brief this plan expands. `BUILD_PLAN.md` (or `MILESTONES.md`) is the same sequence broken into steps and checks; where the two disagree, the plan wins. Build me a local grammar-checking client around the existing LanguageTool engine as a bounded replacement for LanguageTool. Build it in phases in the order below. Do not write the whole product in one pass. Finish the current phase, run its Done when checks, fix failures, then continue. Indie mode completes Phases 1-5. Phase 6 is required before shared or public deployment. ### Stack (fixed, do not substitute) - Runtime: Python 3.12+ and Flask. A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. - Storage: SQLite through Python sqlite3. Local ownership with explicit transactions and a portable backup path. - Interface: Jinja HTML templates, CSS and vanilla JavaScript. Implement the specified screens directly with labelled controls and progressive enhancement. - Verification: Python unittest and browser acceptance fixtures. Check invariants and the actual user workflow before the next phase. - Grammar: LanguageTool Java HTTP server. Reuse existing local language rules rather than writing a grammar engine. ### Data model (create this before Phase 1) - documents(id,path,text,revision) - results(document_id,revision,rule_id,offset,length,message,replacements_json) - ignored_rules(rule_id) - dictionary(word) - profiles(id,language,disabled_rules_json) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. ### Prerequisites and configuration - Python 3.12 or newer and a terminal: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. Cost: Python is free; this local prototype requires no hosting subscription. - A current desktop browser and text editor: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. Verify: You can create a text file in the project folder and open browser developer tools. Cost: No paid service required for this local step. - LanguageTool acceptance sample: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. Cost: No paid service required for this local step. - Confirm the bounded replacement: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. Cost: No paid service required for this local step. - LanguageTool Java HTTP server: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. Cost: Local basic engine; premium cloud-only rules are outside this build. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_URL, LANGUAGETOOL_URL. Ship .env.example, replace placeholders and exclude secrets from git. ### Phase 1 · Local foundation and data contract Build: Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. - Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Commands, after creating the named files: `python -m venv .venv`; `python -m pip install Flask Flask-WTF python-dotenv waitress tzdata`; `python -m pip freeze > requirements.txt`; `python -m flask --app app run --host 127.0.0.1`. - For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Commands, after creating the named files: `python manage.py init`; `python manage.py seed`. - Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. - In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Commands, after creating the named files: `java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081`. - Trap: The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. Done when: The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. Restarting preserves the seeded records or canonical files; running init again deletes nothing. A cross-origin state-changing request to an admin route is refused. Do not build yet: Do not start deploy the grammar engine until these checks pass. ### Phase 2 · Deploy the grammar engine Build: Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: A local installation does not include all premium cloud rules or synonyms. Done when: The probe returns structured matches for the intentionally incorrect sample. A stopped engine yields a clear unavailable status instead of an empty success result. Do not build yet: Do not start review suggestions until these checks pass. ### Phase 3 · Review suggestions Build: Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Do not blindly apply all offsets after the first replacement has changed text length. Done when: The protected noun disappears from displayed issues while other errors remain. Accepting one suggestion triggers a fresh check against the revised text. Do not build yet: Do not start file workflow until these checks pass. ### Phase 4 · File workflow Build: File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Browser extensions and every-language quality parity are beyond this local engine client. Done when: The fixture CLI exits nonzero and identifies the relevant source line. An engine timeout cannot be mistaken for a clean document in CI. Do not build yet: Do not start portable delivery and clean-start proof until these checks pass. ### Phase 5 · Portable delivery and clean-start proof Build: Finish the LanguageTool replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. - Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. - Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Commands, after creating the named files: `python -m pip install -r requirements.txt`; `python -m unittest discover -s test`. - Trap: Export without an import or restore rehearsal is not verified portability. Done when: A new folder reaches the documented fixture outcome using only README instructions. A backup restored in isolation preserves the proof data and can be opened. Do not build yet: Do not start operate and share with access controls until these checks pass. ### Phase 6 · Operate and share with access controls (product-builder only) Build: Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. - Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. - Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Commands, after creating the named files: `waitress-serve --listen=127.0.0.1:5000 app:app`. - Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. - Trap: Product mode is additional engineering, not a switch that makes a local prototype production-ready. Done when: An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. Do not build yet: Do not add new providers or vendor-scale features until the release gates are recorded. ### Out of scope (and why) - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set: retain the paid product if this capability is essential; it is outside this bounded build. - hosted server convenience: retain the paid product if this capability is essential; it is outside this bounded build. - browser extension premium flows: retain the paid product if this capability is essential; it is outside this bounded build. - team/admin options: retain the paid product if this capability is essential; it is outside this bounded build. - support: retain the paid product if this capability is essential; it is outside this bounded build. ### README must contain - Exact install, configure, initialize, start, test and recovery commands for a clean folder. - The schema, fixture, acceptance results, private data locations and export format. - Every prerequisite and secret source, actual running costs and deliberately excluded capabilities. - An honest distinction between tests actually run and checks still awaiting real credentials or deployment. - Dependency documentation reviewed 2026-09-05: https://flask.palletsprojects.com/en/stable/installation/ ; https://docs.python.org/3/library/sqlite3.html ; https://flask-wtf.readthedocs.io/en/1.2.x/csrf/ ; https://docs.pylonsproject.org/projects/waitress/en/stable/usage.html ; https://dev.languagetool.org/http-server. Recheck before upgrading; freeze the versions actually tested. ===== ARCHITECTURE.md ===== # Architecture · LanguageTool ## Stack | Part | Choice | Why | | --- | --- | --- | | Runtime | Python 3.12+ and Flask | A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. | | Storage | SQLite through Python sqlite3 | Local ownership with explicit transactions and a portable backup path. | | Interface | Jinja HTML templates, CSS and vanilla JavaScript | Implement the specified screens directly with labelled controls and progressive enhancement. | | Verification | Python unittest and browser acceptance fixtures | Check invariants and the actual user workflow before the next phase. | | Grammar | LanguageTool Java HTTP server | Reuse existing local language rules rather than writing a grammar engine. | ## Modules Each module has one owner concern and a documented way to replace it. | Module | Owns | How to replace it | | --- | --- | --- | | Data and identity | documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json) | Keep stable ids and a versioned repository interface; migrate storage behind it with a fixture round-trip. | | Deploy the grammar engine | Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. | Keep the acceptance contract: The probe returns structured matches for the intentionally incorrect sample. Replace only this module after replaying its fixture; preserve ids and existing records. | | Review suggestions | Build document input, grouped rule results and click-to-apply suggestions against an exact revision. | Keep the acceptance contract: The protected noun disappears from displayed issues while other errors remain. Replace only this module after replaying its fixture; preserve ids and existing records. | | File workflow | Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. | Keep the acceptance contract: The fixture CLI exits nonzero and identifies the relevant source line. Replace only this module after replaying its fixture; preserve ids and existing records. | | Operations | Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. | Change host or backup destination only after an isolated restore and authorization check. | ## Configuration Every runtime setting is an environment variable documented in `.env.example`, validated at startup, with a safe local default wherever one exists. - `DATA_DIR` · required · A new private folder this application creates on first run; never serve it as static files. - `SECRET_KEY` · required, secret · Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. - `APP_MODE` · required · Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. - `SITE_URL` · required · The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. - `LANGUAGETOOL_URL` · required · The loopback Java server started from the official distribution; Python proxies checks to it. ## Production baseline - Security: least privilege, input validation at every boundary, secret redaction in logs, rate limits on abuse-prone paths, no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore procedures that have been exercised. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, timeouts. - Observability: structured logs with request or operation ids, an error-tracking hook, and health and readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, one end-to-end test of the critical path. ## Decision records For each dependency in the stack table, keep a short note: why it was chosen, its failure mode, and how it is replaced. Do not add infrastructure until a requirement in `PRODUCT.md` justifies it. ===== AGENTS.md ===== # Agent instructions · LanguageTool product build - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. The stack is fixed: Python 3.12+ and Flask, SQLite through Python sqlite3, Jinja HTML templates, CSS and vanilla JavaScript, Python unittest and browser acceptance fixtures, LanguageTool Java HTTP server. - Implement milestone by milestone from `MILESTONES.md`; keep each change reviewable and leave the application runnable at every commit. - Treat authentication, payments, encryption, imports, webhooks and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a check pass. - Put every external service behind an interface with a deterministic fake for tests. - Add migrations and rollback or recovery notes for every persistent data change. - Log useful operational context without credentials, tokens, passwords or personal data. - Update documentation and run every check before completing a milestone. ## Known traps - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. - A local installation does not include all premium cloud rules or synonyms. - Do not blindly apply all offsets after the first replacement has changed text length. - Browser extensions and every-language quality parity are beyond this local engine client. - Export without an import or restore rehearsal is not verified portability. - Product mode is additional engineering, not a switch that makes a local prototype production-ready. ===== MILESTONES.md ===== # Delivery milestones · LanguageTool Estimated effort: **multi-day** for the indie phases; the production-only milestones add the trust and operability layer. ## M1 · Local foundation and data contract Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. ### Steps 1. Create and start the local application Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Files: `app.py`, `requirements.txt`, `.env.example`, `.gitignore` ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` 2. Implement storage and seed the named fixture For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Files: `schema.sql`, `db.py`, `manage.py`, `fixtures/README.md` ```sh python manage.py init python manage.py seed ``` 3. Protect the local request boundary Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. Files: `app.py`, `templates/base.html`, `test/test_foundation.py` 4. Start the grammar service In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Files: `providers/grammar.py` ```sh java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081 ``` ### Done when - [ ] The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. - [ ] Restarting preserves the seeded records or canonical files; running init again deletes nothing. - [ ] A cross-origin state-changing request to an admin route is refused. ### Watch out - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. ## M2 · Deploy the grammar engine Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. ### Steps 1. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Files: `features/deploy_the_grammar_engine.py`, `templates/deploy_the_grammar_engine.html` 2. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Files: `features/deploy_the_grammar_engine.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_deploy_the_grammar_engine.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] A stopped engine yields a clear unavailable status instead of an empty success result. ### Watch out - A local installation does not include all premium cloud rules or synonyms. ## M3 · Review suggestions Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. ### Steps 1. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Files: `features/review_suggestions.py`, `templates/review_suggestions.html` 2. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Files: `features/review_suggestions.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_review_suggestions.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] Accepting one suggestion triggers a fresh check against the revised text. ### Watch out - Do not blindly apply all offsets after the first replacement has changed text length. ## M4 · File workflow File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. ### Steps 1. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Files: `features/file_workflow.py`, `templates/file_workflow.html` 2. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Files: `features/file_workflow.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_file_workflow.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] An engine timeout cannot be mistaken for a clean document in CI. ### Watch out - Browser extensions and every-language quality parity are beyond this local engine client. ## M5 · Portable delivery and clean-start proof Finish the LanguageTool replacement with a documented first-run path and a recoverable export. ### Steps 1. Write the user-facing runbook Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. Files: `README.md`, `.env.example` 2. Create and rehearse a recoverable backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Files: `OPERATIONS.md`, `scripts/backup.py` 3. Reproduce the first-run experience Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Files: `README.md`, `verification.md` ```sh python -m pip install -r requirements.txt python -m unittest discover -s test ``` ### Done when - [ ] A new folder reaches the documented fixture outcome using only README instructions. - [ ] A backup restored in isolation preserves the proof data and can be opened. ### Watch out - Export without an import or restore rehearsal is not verified portability. ## M6 · Operate and share with access controls (production only) Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. ### Steps 1. Establish owner access before exposure Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. Files: `ARCHITECTURE.md`, `OPERATIONS.md` 2. Deploy a monitored service Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Files: `Caddyfile`, `OPERATIONS.md` ```sh waitress-serve --listen=127.0.0.1:5000 app:app ``` 3. Rehearse loss and recovery Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. Files: `OPERATIONS.md`, `release-checklist.md` ### Done when - [ ] An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. - [ ] A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. - [ ] Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. ### Watch out - Product mode is additional engineering, not a switch that makes a local prototype production-ready. ===== OPERATIONS.md ===== # Operations · LanguageTool ## Backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. ## Restore Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Do a restore drill before the first real user, and write the date here when it passes. ## Monitoring Check HTTP availability, free disk, backup age and domain failures for Deploy the grammar engine, Review suggestions, File workflow. Alert without logging private payloads. ## Incident checklist Pause outbound jobs and public intake as appropriate, preserve redacted diagnostics, restore an isolated verified backup, rotate affected credentials and reconcile uncertain effects before reopening. 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified backup when needed. 5. Document the root cause, the remediation and the regression test. ## Release gate - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] A private-data authorization check passes using a second identity. - [ ] An off-host restore drill has a date, observed result and recovery time in OPERATIONS.md. - [ ] README names the excluded vendor capabilities and actual provider/hosting costs. ## Launch constraint Do not market omitted LanguageTool capabilities as implemented. The non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered. ===== .env.example ===== # Copy to .env and fill in. Never commit .env; this file documents it. # Required. A new private folder this application creates on first run; never serve it as static files. DATA_DIR=./data/languagetool # Required · secret. Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. SECRET_KEY=replace-with-a-random-secret # Required. Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. APP_MODE=indie # Required. The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. SITE_URL=http://127.0.0.1:5000 # Required. The loopback Java server started from the official distribution; Python proxies checks to it. LANGUAGETOOL_URL=http://127.0.0.1:8081
README.md
# LanguageTool · indie build Build a local grammar-checking client around the existing LanguageTool engine. Estimated effort: **multi-day**. Work `BUILD_PLAN.md` top to bottom · every phase ends in a check that has to pass before the next one starts. ## Stack | Part | Choice | Why | | --- | --- | --- | | Runtime | Python 3.12+ and Flask | A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. | | Storage | SQLite through Python sqlite3 | Local ownership with explicit transactions and a portable backup path. | | Interface | Jinja HTML templates, CSS and vanilla JavaScript | Implement the specified screens directly with labelled controls and progressive enhancement. | | Verification | Python unittest and browser acceptance fixtures | Check invariants and the actual user workflow before the next phase. | | Grammar | LanguageTool Java HTTP server | Reuse existing local language rules rather than writing a grammar engine. | ## Before you start Have every one of these ready. The plan assumes them from step one. - [ ] **Python 3.12 or newer and a terminal** · Python is free; this local prototype requires no hosting subscription. - Why: The local backend, tests and storage use Python. - Get it: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. - Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. - [ ] **A current desktop browser and text editor** · No paid service required for this local step. - Why: You need to view the local interface and edit generated project files. - Get it: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. - Verify: You can create a text file in the project folder and open browser developer tools. - [ ] **LanguageTool acceptance sample** · No paid service required for this local step. - Why: This fixture makes the first end-to-end check reproducible. - Get it: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. - Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. - [ ] **Confirm the bounded replacement** · No paid service required for this local step. - Why: The full commercial product is larger than this guide. - Get it: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. - Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. - [ ] **LanguageTool Java HTTP server** · Local basic engine; premium cloud-only rules are outside this build. - Why: The app reuses the existing grammar engine. - Get it: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. - Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. ## Quick start ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` Then copy `.env.example` to `.env` and fill in the values it documents. ## Honest limits This build deliberately does not replace: - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support If one of those is essential to you, that is the reason to keep paying for LanguageTool, and the README should say so rather than pretend.
BRIEF.md
# Build brief · LanguageTool The one-shot brief this plan expands. `BUILD_PLAN.md` (or `MILESTONES.md`) is the same sequence broken into steps and checks; where the two disagree, the plan wins. Build me a local grammar-checking client around the existing LanguageTool engine as a bounded replacement for LanguageTool. Build it in phases in the order below. Do not write the whole product in one pass. Finish the current phase, run its Done when checks, fix failures, then continue. Indie mode completes Phases 1-5. Phase 6 is required before shared or public deployment. ### Stack (fixed, do not substitute) - Runtime: Python 3.12+ and Flask. A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. - Storage: SQLite through Python sqlite3. Local ownership with explicit transactions and a portable backup path. - Interface: Jinja HTML templates, CSS and vanilla JavaScript. Implement the specified screens directly with labelled controls and progressive enhancement. - Verification: Python unittest and browser acceptance fixtures. Check invariants and the actual user workflow before the next phase. - Grammar: LanguageTool Java HTTP server. Reuse existing local language rules rather than writing a grammar engine. ### Data model (create this before Phase 1) - documents(id,path,text,revision) - results(document_id,revision,rule_id,offset,length,message,replacements_json) - ignored_rules(rule_id) - dictionary(word) - profiles(id,language,disabled_rules_json) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. ### Prerequisites and configuration - Python 3.12 or newer and a terminal: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. Cost: Python is free; this local prototype requires no hosting subscription. - A current desktop browser and text editor: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. Verify: You can create a text file in the project folder and open browser developer tools. Cost: No paid service required for this local step. - LanguageTool acceptance sample: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. Cost: No paid service required for this local step. - Confirm the bounded replacement: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. Cost: No paid service required for this local step. - LanguageTool Java HTTP server: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. Cost: Local basic engine; premium cloud-only rules are outside this build. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_URL, LANGUAGETOOL_URL. Ship .env.example, replace placeholders and exclude secrets from git. ### Phase 1 · Local foundation and data contract Build: Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. - Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Commands, after creating the named files: `python -m venv .venv`; `python -m pip install Flask Flask-WTF python-dotenv waitress tzdata`; `python -m pip freeze > requirements.txt`; `python -m flask --app app run --host 127.0.0.1`. - For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Commands, after creating the named files: `python manage.py init`; `python manage.py seed`. - Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. - In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Commands, after creating the named files: `java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081`. - Trap: The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. Done when: The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. Restarting preserves the seeded records or canonical files; running init again deletes nothing. A cross-origin state-changing request to an admin route is refused. Do not build yet: Do not start deploy the grammar engine until these checks pass. ### Phase 2 · Deploy the grammar engine Build: Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: A local installation does not include all premium cloud rules or synonyms. Done when: The probe returns structured matches for the intentionally incorrect sample. A stopped engine yields a clear unavailable status instead of an empty success result. Do not build yet: Do not start review suggestions until these checks pass. ### Phase 3 · Review suggestions Build: Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Do not blindly apply all offsets after the first replacement has changed text length. Done when: The protected noun disappears from displayed issues while other errors remain. Accepting one suggestion triggers a fresh check against the revised text. Do not build yet: Do not start file workflow until these checks pass. ### Phase 4 · File workflow Build: File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Browser extensions and every-language quality parity are beyond this local engine client. Done when: The fixture CLI exits nonzero and identifies the relevant source line. An engine timeout cannot be mistaken for a clean document in CI. Do not build yet: Do not start portable delivery and clean-start proof until these checks pass. ### Phase 5 · Portable delivery and clean-start proof Build: Finish the LanguageTool replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. - Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. - Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Commands, after creating the named files: `python -m pip install -r requirements.txt`; `python -m unittest discover -s test`. - Trap: Export without an import or restore rehearsal is not verified portability. Done when: A new folder reaches the documented fixture outcome using only README instructions. A backup restored in isolation preserves the proof data and can be opened. Do not build yet: Do not start operate and share with access controls until these checks pass. ### Phase 6 · Operate and share with access controls (product-builder only) Build: Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. - Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. - Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Commands, after creating the named files: `waitress-serve --listen=127.0.0.1:5000 app:app`. - Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. - Trap: Product mode is additional engineering, not a switch that makes a local prototype production-ready. Done when: An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. Do not build yet: Do not add new providers or vendor-scale features until the release gates are recorded. ### Out of scope (and why) - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set: retain the paid product if this capability is essential; it is outside this bounded build. - hosted server convenience: retain the paid product if this capability is essential; it is outside this bounded build. - browser extension premium flows: retain the paid product if this capability is essential; it is outside this bounded build. - team/admin options: retain the paid product if this capability is essential; it is outside this bounded build. - support: retain the paid product if this capability is essential; it is outside this bounded build. ### README must contain - Exact install, configure, initialize, start, test and recovery commands for a clean folder. - The schema, fixture, acceptance results, private data locations and export format. - Every prerequisite and secret source, actual running costs and deliberately excluded capabilities. - An honest distinction between tests actually run and checks still awaiting real credentials or deployment. - Dependency documentation reviewed 2026-09-05: https://flask.palletsprojects.com/en/stable/installation/ ; https://docs.python.org/3/library/sqlite3.html ; https://flask-wtf.readthedocs.io/en/1.2.x/csrf/ ; https://docs.pylonsproject.org/projects/waitress/en/stable/usage.html ; https://dev.languagetool.org/http-server. Recheck before upgrading; freeze the versions actually tested.
AGENTS.md
# Agent instructions · LanguageTool indie build - Read `README.md` and `BUILD_PLAN.md` before writing code. The stack is fixed: Python 3.12+ and Flask, SQLite through Python sqlite3, Jinja HTML templates, CSS and vanilla JavaScript, Python unittest and browser acceptance fixtures, LanguageTool Java HTTP server. Do not substitute. - Work one phase at a time, in order. Do not start a phase until every "Done when" item of the previous one passes. - Prefer the fewest moving parts that satisfy the step. No frameworks, services or dependencies the plan does not name. - Secrets live in `.env`, never in source or logs. Keep `.env.example` current when a variable is introduced. - Do not invent cryptography, security guarantees, APIs or compliance claims. - Add a focused test for every destructive, security-sensitive or data-loss path the plan names. - Run the project checks before declaring a phase complete, and record any deliberate shortcut in the README under "Tradeoffs". ## Known traps - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. - A local installation does not include all premium cloud rules or synonyms. - Do not blindly apply all offsets after the first replacement has changed text length. - Browser extensions and every-language quality parity are beyond this local engine client. - Export without an import or restore rehearsal is not verified portability.
BUILD_PLAN.md
# Build plan · LanguageTool Build a local grammar-checking client around the existing LanguageTool engine. Phases are in dependency order. Each ends in a "Done when" list; treat an unticked item as a blocker, not a note. ## Phase 1 · Local foundation and data contract Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. ### Steps 1. Create and start the local application Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Files: `app.py`, `requirements.txt`, `.env.example`, `.gitignore` ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` 2. Implement storage and seed the named fixture For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Files: `schema.sql`, `db.py`, `manage.py`, `fixtures/README.md` ```sh python manage.py init python manage.py seed ``` 3. Protect the local request boundary Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. Files: `app.py`, `templates/base.html`, `test/test_foundation.py` 4. Start the grammar service In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Files: `providers/grammar.py` ```sh java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081 ``` ### Done when - [ ] The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. - [ ] Restarting preserves the seeded records or canonical files; running init again deletes nothing. - [ ] A cross-origin state-changing request to an admin route is refused. ### Watch out - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. ## Phase 2 · Deploy the grammar engine Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. ### Steps 1. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Files: `features/deploy_the_grammar_engine.py`, `templates/deploy_the_grammar_engine.html` 2. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Files: `features/deploy_the_grammar_engine.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_deploy_the_grammar_engine.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] A stopped engine yields a clear unavailable status instead of an empty success result. ### Watch out - A local installation does not include all premium cloud rules or synonyms. ## Phase 3 · Review suggestions Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. ### Steps 1. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Files: `features/review_suggestions.py`, `templates/review_suggestions.html` 2. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Files: `features/review_suggestions.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_review_suggestions.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] Accepting one suggestion triggers a fresh check against the revised text. ### Watch out - Do not blindly apply all offsets after the first replacement has changed text length. ## Phase 4 · File workflow File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. ### Steps 1. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Files: `features/file_workflow.py`, `templates/file_workflow.html` 2. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Files: `features/file_workflow.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_file_workflow.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] An engine timeout cannot be mistaken for a clean document in CI. ### Watch out - Browser extensions and every-language quality parity are beyond this local engine client. ## Phase 5 · Portable delivery and clean-start proof Finish the LanguageTool replacement with a documented first-run path and a recoverable export. ### Steps 1. Write the user-facing runbook Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. Files: `README.md`, `.env.example` 2. Create and rehearse a recoverable backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Files: `OPERATIONS.md`, `scripts/backup.py` 3. Reproduce the first-run experience Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Files: `README.md`, `verification.md` ```sh python -m pip install -r requirements.txt python -m unittest discover -s test ``` ### Done when - [ ] A new folder reaches the documented fixture outcome using only README instructions. - [ ] A backup restored in isolation preserves the proof data and can be opened. ### Watch out - Export without an import or restore rehearsal is not verified portability. ## Not in this build - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support ## After v1, if you want it - Extend file workflow only after the fixture and recovery checks pass. - Before adding another integration, document its credentials, limits, cost and failure behavior with primary-source links.
.env.example
# Copy to .env and fill in. Never commit .env; this file documents it. # Required. A new private folder this application creates on first run; never serve it as static files. DATA_DIR=./data/languagetool # Required · secret. Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. SECRET_KEY=replace-with-a-random-secret # Required. Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. APP_MODE=indie # Required. The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. SITE_URL=http://127.0.0.1:5000 # Required. The loopback Java server started from the official distribution; Python proxies checks to it. LANGUAGETOOL_URL=http://127.0.0.1:8081
PRODUCT.md
# LanguageTool · product brief ## Problem Because the core engine is open source, a technical user can run the grammar checker locally or self-host the server for many use cases. ## Product outcome An operated version of a local grammar-checking client around the existing LanguageTool engine, with authorized access, restore evidence and honest scope limits. ## Target user A builder who needs a maintainable product foundation, not a one-off demo. ## Required capabilities - Java runtime or Docker - local/server install - browser/editor integration - optional LLM API ## Explicit non-goals for v1 - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set - hosted server convenience - browser extension premium flows - team/admin options - support ## Success criteria - The probe returns structured matches for the intentionally incorrect sample. - The protected noun disappears from displayed issues while other errors remain. - The fixture CLI exits nonzero and identifies the relevant source line. - A private-data authorization check passes using a second identity. - An off-host restore drill has a date, observed result and recovery time in OPERATIONS.md. - README names the excluded vendor capabilities and actual provider/hosting costs.
BRIEF.md
# Build brief · LanguageTool The one-shot brief this plan expands. `BUILD_PLAN.md` (or `MILESTONES.md`) is the same sequence broken into steps and checks; where the two disagree, the plan wins. Build me a local grammar-checking client around the existing LanguageTool engine as a bounded replacement for LanguageTool. Build it in phases in the order below. Do not write the whole product in one pass. Finish the current phase, run its Done when checks, fix failures, then continue. Indie mode completes Phases 1-5. Phase 6 is required before shared or public deployment. ### Stack (fixed, do not substitute) - Runtime: Python 3.12+ and Flask. A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. - Storage: SQLite through Python sqlite3. Local ownership with explicit transactions and a portable backup path. - Interface: Jinja HTML templates, CSS and vanilla JavaScript. Implement the specified screens directly with labelled controls and progressive enhancement. - Verification: Python unittest and browser acceptance fixtures. Check invariants and the actual user workflow before the next phase. - Grammar: LanguageTool Java HTTP server. Reuse existing local language rules rather than writing a grammar engine. ### Data model (create this before Phase 1) - documents(id,path,text,revision) - results(document_id,revision,rule_id,offset,length,message,replacements_json) - ignored_rules(rule_id) - dictionary(word) - profiles(id,language,disabled_rules_json) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. ### Prerequisites and configuration - Python 3.12 or newer and a terminal: Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. Verify: python --version reports 3.12+ and python -c "import sqlite3" succeeds. Cost: Python is free; this local prototype requires no hosting subscription. - A current desktop browser and text editor: Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository. Verify: You can create a text file in the project folder and open browser developer tools. Cost: No paid service required for this local step. - LanguageTool acceptance sample: Create synthetic sample data before implementation: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records. Verify: The fixture files and expected outcomes exist and can be inspected without a vendor account. Cost: No paid service required for this local step. - Confirm the bounded replacement: The deliverable is a local grammar-checking client around the existing LanguageTool engine. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first. Verify: You can name the primary workflow and the vendor features this version deliberately leaves out. Cost: No paid service required for this local step. - LanguageTool Java HTTP server: Follow the official server page: install a compatible Java runtime, download and unzip the LanguageTool server distribution, create server.properties, then start the server using the command in the first phase. Do not install an assumed official Docker image. Verify: java -version succeeds and http://localhost:8081/v2/check?language=en-US&text=my+text returns JSON. Cost: Local basic engine; premium cloud-only rules are outside this build. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_URL, LANGUAGETOOL_URL. Ship .env.example, replace placeholders and exclude secrets from git. ### Phase 1 · Local foundation and data contract Build: Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. - Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Commands, after creating the named files: `python -m venv .venv`; `python -m pip install Flask Flask-WTF python-dotenv waitress tzdata`; `python -m pip freeze > requirements.txt`; `python -m flask --app app run --host 127.0.0.1`. - For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Commands, after creating the named files: `python manage.py init`; `python manage.py seed`. - Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. - In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Commands, after creating the named files: `java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081`. - Trap: The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. Done when: The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. Restarting preserves the seeded records or canonical files; running init again deletes nothing. A cross-origin state-changing request to an admin route is refused. Do not build yet: Do not start deploy the grammar engine until these checks pass. ### Phase 2 · Deploy the grammar engine Build: Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. - Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: A local installation does not include all premium cloud rules or synonyms. Done when: The probe returns structured matches for the intentionally incorrect sample. A stopped engine yields a clear unavailable status instead of an empty success result. Do not build yet: Do not start review suggestions until these checks pass. ### Phase 3 · Review suggestions Build: Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Build document input, grouped rule results and click-to-apply suggestions against an exact revision. - Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Do not blindly apply all offsets after the first replacement has changed text length. Done when: The protected noun disappears from displayed issues while other errors remain. Accepting one suggestion triggers a fresh check against the revised text. Do not build yet: Do not start file workflow until these checks pass. ### Phase 4 · File workflow Build: File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. - Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. - Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Commands, after creating the named files: `python -m unittest discover -s test`. - Trap: Browser extensions and every-language quality parity are beyond this local engine client. Done when: The fixture CLI exits nonzero and identifies the relevant source line. An engine timeout cannot be mistaken for a clean document in CI. Do not build yet: Do not start portable delivery and clean-start proof until these checks pass. ### Phase 5 · Portable delivery and clean-start proof Build: Finish the LanguageTool replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. - Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. - Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Commands, after creating the named files: `python -m pip install -r requirements.txt`; `python -m unittest discover -s test`. - Trap: Export without an import or restore rehearsal is not verified portability. Done when: A new folder reaches the documented fixture outcome using only README instructions. A backup restored in isolation preserves the proof data and can be opened. Do not build yet: Do not start operate and share with access controls until these checks pass. ### Phase 6 · Operate and share with access controls (product-builder only) Build: Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. - Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. - Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Commands, after creating the named files: `waitress-serve --listen=127.0.0.1:5000 app:app`. - Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. - Trap: Product mode is additional engineering, not a switch that makes a local prototype production-ready. Done when: An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. Do not build yet: Do not add new providers or vendor-scale features until the release gates are recorded. ### Out of scope (and why) - Browser extensions and every-language quality parity are beyond this local engine client. - This is a local grammar-checking client around the existing LanguageTool engine; full LanguageTool feature parity, vendor hosting and support are not included. - premium rule set: retain the paid product if this capability is essential; it is outside this bounded build. - hosted server convenience: retain the paid product if this capability is essential; it is outside this bounded build. - browser extension premium flows: retain the paid product if this capability is essential; it is outside this bounded build. - team/admin options: retain the paid product if this capability is essential; it is outside this bounded build. - support: retain the paid product if this capability is essential; it is outside this bounded build. ### README must contain - Exact install, configure, initialize, start, test and recovery commands for a clean folder. - The schema, fixture, acceptance results, private data locations and export format. - Every prerequisite and secret source, actual running costs and deliberately excluded capabilities. - An honest distinction between tests actually run and checks still awaiting real credentials or deployment. - Dependency documentation reviewed 2026-09-05: https://flask.palletsprojects.com/en/stable/installation/ ; https://docs.python.org/3/library/sqlite3.html ; https://flask-wtf.readthedocs.io/en/1.2.x/csrf/ ; https://docs.pylonsproject.org/projects/waitress/en/stable/usage.html ; https://dev.languagetool.org/http-server. Recheck before upgrading; freeze the versions actually tested.
ARCHITECTURE.md
# Architecture · LanguageTool ## Stack | Part | Choice | Why | | --- | --- | --- | | Runtime | Python 3.12+ and Flask | A small local HTTP application with readable server-side handlers and no JavaScript build pipeline. | | Storage | SQLite through Python sqlite3 | Local ownership with explicit transactions and a portable backup path. | | Interface | Jinja HTML templates, CSS and vanilla JavaScript | Implement the specified screens directly with labelled controls and progressive enhancement. | | Verification | Python unittest and browser acceptance fixtures | Check invariants and the actual user workflow before the next phase. | | Grammar | LanguageTool Java HTTP server | Reuse existing local language rules rather than writing a grammar engine. | ## Modules Each module has one owner concern and a documented way to replace it. | Module | Owns | How to replace it | | --- | --- | --- | | Data and identity | documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json) | Keep stable ids and a versioned repository interface; migrate storage behind it with a fixture round-trip. | | Deploy the grammar engine | Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. | Keep the acceptance contract: The probe returns structured matches for the intentionally incorrect sample. Replace only this module after replaying its fixture; preserve ids and existing records. | | Review suggestions | Build document input, grouped rule results and click-to-apply suggestions against an exact revision. | Keep the acceptance contract: The protected noun disappears from displayed issues while other errors remain. Replace only this module after replaying its fixture; preserve ids and existing records. | | File workflow | Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. | Keep the acceptance contract: The fixture CLI exits nonzero and identifies the relevant source line. Replace only this module after replaying its fixture; preserve ids and existing records. | | Operations | Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. | Change host or backup destination only after an isolated restore and authorization check. | ## Configuration Every runtime setting is an environment variable documented in `.env.example`, validated at startup, with a safe local default wherever one exists. - `DATA_DIR` · required · A new private folder this application creates on first run; never serve it as static files. - `SECRET_KEY` · required, secret · Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. - `APP_MODE` · required · Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. - `SITE_URL` · required · The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. - `LANGUAGETOOL_URL` · required · The loopback Java server started from the official distribution; Python proxies checks to it. ## Production baseline - Security: least privilege, input validation at every boundary, secret redaction in logs, rate limits on abuse-prone paths, no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore procedures that have been exercised. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, timeouts. - Observability: structured logs with request or operation ids, an error-tracking hook, and health and readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, one end-to-end test of the critical path. ## Decision records For each dependency in the stack table, keep a short note: why it was chosen, its failure mode, and how it is replaced. Do not add infrastructure until a requirement in `PRODUCT.md` justifies it.
AGENTS.md
# Agent instructions · LanguageTool product build - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. The stack is fixed: Python 3.12+ and Flask, SQLite through Python sqlite3, Jinja HTML templates, CSS and vanilla JavaScript, Python unittest and browser acceptance fixtures, LanguageTool Java HTTP server. - Implement milestone by milestone from `MILESTONES.md`; keep each change reviewable and leave the application runnable at every commit. - Treat authentication, payments, encryption, imports, webhooks and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a check pass. - Put every external service behind an interface with a deterministic fake for tests. - Add migrations and rollback or recovery notes for every persistent data change. - Log useful operational context without credentials, tokens, passwords or personal data. - Update documentation and run every check before completing a milestone. ## Known traps - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. - A local installation does not include all premium cloud rules or synonyms. - Do not blindly apply all offsets after the first replacement has changed text length. - Browser extensions and every-language quality parity are beyond this local engine client. - Export without an import or restore rehearsal is not verified portability. - Product mode is additional engineering, not a switch that makes a local prototype production-ready.
MILESTONES.md
# Delivery milestones · LanguageTool Estimated effort: **multi-day** for the indie phases; the production-only milestones add the trust and operability layer. ## M1 · Local foundation and data contract Create the runnable local foundation for a local grammar-checking client around the existing LanguageTool engine, including the real schema and acceptance fixture. ### Steps 1. Create and start the local application Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch. Files: `app.py`, `requirements.txt`, `.env.example`, `.gitignore` ```sh python -m venv .venv python -m pip install Flask Flask-WTF python-dotenv waitress tzdata python -m pip freeze > requirements.txt python -m flask --app app run --host 127.0.0.1 ``` 2. Implement storage and seed the named fixture For database-backed state create schema.sql and db.py using this model: documents(id,path,text,revision); results(document_id,revision,rule_id,offset,length,message,replacements_json); ignored_rules(rule_id); dictionary(word); profiles(id,language,disabled_rules_json). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Re-running init must not erase data. Files: `schema.sql`, `db.py`, `manage.py`, `fixtures/README.md` ```sh python manage.py init python manage.py seed ``` 3. Protect the local request boundary Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now. Files: `app.py`, `templates/base.html`, `test/test_foundation.py` 4. Start the grammar service In the unzipped LanguageTool distribution create an empty server.properties and run the command below in a separate terminal. Keep the default local-only binding and proxy requests through Python; no browser-wide CORS permission is needed. Files: `providers/grammar.py` ```sh java -cp languagetool-server.jar org.languagetool.server.HTTPServer --config server.properties --port 8081 ``` ### Done when - [ ] The documented start command serves /healthz and the initial screen at http://127.0.0.1:5000. - [ ] Restarting preserves the seeded records or canonical files; running init again deletes nothing. - [ ] A cross-origin state-changing request to an admin route is refused. ### Watch out - The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation. ## M2 · Deploy the grammar engine Deploy the grammar engine for LanguageTool: Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. ### Steps 1. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Download the official LanguageTool server distribution and start its Java HTTP service on loopback; do not implement a grammar engine. Files: `features/deploy_the_grammar_engine.py`, `templates/deploy_the_grammar_engine.html` 2. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Create a backend health probe that performs a short /v2/check request with an explicit language and a timeout. Files: `features/deploy_the_grammar_engine.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The probe returns structured matches for the intentionally incorrect sample. Then check: A stopped engine yields a clear unavailable status instead of an empty success result. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_deploy_the_grammar_engine.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] A stopped engine yields a clear unavailable status instead of an empty success result. ### Watch out - A local installation does not include all premium cloud rules or synonyms. ## M3 · Review suggestions Review suggestions for LanguageTool: Build document input, grouped rule results and click-to-apply suggestions against an exact revision. ### Steps 1. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Build document input, grouped rule results and click-to-apply suggestions against an exact revision. Files: `features/review_suggestions.py`, `templates/review_suggestions.html` 2. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Add a local dictionary and ignored-rule profile that filters results without modifying the original source until acceptance. Files: `features/review_suggestions.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The protected noun disappears from displayed issues while other errors remain. Then check: Accepting one suggestion triggers a fresh check against the revised text. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_review_suggestions.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] Accepting one suggestion triggers a fresh check against the revised text. ### Watch out - Do not blindly apply all offsets after the first replacement has changed text length. ## M4 · File workflow File workflow for LanguageTool: Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. ### Steps 1. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Build a CLI for plain-text and Markdown input that reports line/column locations and an issue count. Files: `features/file_workflow.py`, `templates/file_workflow.html` 2. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Export JSON results with rule ids, language and engine version; keep the document text out of routine logs. Files: `features/file_workflow.py` 3. Exercise the acceptance fixture Use An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Check: The fixture CLI exits nonzero and identifies the relevant source line. Then check: An engine timeout cannot be mistaken for a clean document in CI. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser. Files: `test/test_file_workflow.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] An engine timeout cannot be mistaken for a clean document in CI. ### Watch out - Browser extensions and every-language quality parity are beyond this local engine client. ## M5 · Portable delivery and clean-start proof Finish the LanguageTool replacement with a documented first-run path and a recoverable export. ### Steps 1. Write the user-facing runbook Document prerequisites, every configuration variable, data locations and the three domain workflows: Deploy the grammar engine; Review suggestions; File workflow. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them. Files: `README.md`, `.env.example` 2. Create and rehearse a recoverable backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Files: `OPERATIONS.md`, `scripts/backup.py` 3. Reproduce the first-run experience Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete. Files: `README.md`, `verification.md` ```sh python -m pip install -r requirements.txt python -m unittest discover -s test ``` ### Done when - [ ] A new folder reaches the documented fixture outcome using only README instructions. - [ ] A backup restored in isolation preserves the proof data and can be opened. ### Watch out - Export without an import or restore rehearsal is not verified portability. ## M6 · Operate and share with access controls (production only) Product-builder only: make a local grammar-checking client around the existing LanguageTool engine operable for its stated audience before allowing remote access. ### Steps 1. Establish owner access before exposure Add owner/member/viewer authorization as appropriate, password hashes through Werkzeug, expiring server-side sessions, CSRF and login rate limits. Add users, sessions and audit schema migrations plus an owner-init command prompting securely for its password. Enforce ownership on every private read/write/download and fail closed when product setup is incomplete. Existing learner or admin identities must use this same boundary. Files: `ARCHITECTURE.md`, `OPERATIONS.md` 2. Deploy a monitored service Serve app:app with Waitress on loopback behind Caddy HTTPS. Configure trusted proxy headers narrowly, Secure/HttpOnly/SameSite cookies, upload limits, request ids and structured logs without private content. Run background jobs as a separate supervised process when the domain workflow requires them. Files: `Caddyfile`, `OPERATIONS.md` ```sh waitress-serve --listen=127.0.0.1:5000 app:app ``` 3. Rehearse loss and recovery Add a scheduled encrypted off-host backup using this procedure: Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. Monitor health, disk space, failed jobs and backup age. Stop the service to test an alert; rehearse recovery with outbound effects disabled and record recovery time. Rotate any exposed credentials and require reconciliation before resuming queues. Files: `OPERATIONS.md`, `release-checklist.md` ### Done when - [ ] An unauthenticated visitor cannot read private records, download private files or mutate the admin surface. - [ ] A viewer cannot mutate data by making a direct request, and guessed ids cannot cross ownership boundaries. - [ ] Stopping the service triggers the configured alert and the latest off-host backup restores the fixture in isolation. ### Watch out - Product mode is additional engineering, not a switch that makes a local prototype production-ready.
OPERATIONS.md
# Operations · LanguageTool ## Backup Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for languagetool. ## Restore Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: An en-US test document with a misspelling, a repeated word and one domain-specific proper noun. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy. Do a restore drill before the first real user, and write the date here when it passes. ## Monitoring Check HTTP availability, free disk, backup age and domain failures for Deploy the grammar engine, Review suggestions, File workflow. Alert without logging private payloads. ## Incident checklist Pause outbound jobs and public intake as appropriate, preserve redacted diagnostics, restore an isolated verified backup, rotate affected credentials and reconcile uncertain effects before reopening. 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified backup when needed. 5. Document the root cause, the remediation and the regression test. ## Release gate - [ ] The probe returns structured matches for the intentionally incorrect sample. - [ ] The protected noun disappears from displayed issues while other errors remain. - [ ] The fixture CLI exits nonzero and identifies the relevant source line. - [ ] A private-data authorization check passes using a second identity. - [ ] An off-host restore drill has a date, observed result and recovery time in OPERATIONS.md. - [ ] README names the excluded vendor capabilities and actual provider/hosting costs. ## Launch constraint Do not market omitted LanguageTool capabilities as implemented. The non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
.env.example
# Copy to .env and fill in. Never commit .env; this file documents it. # Required. A new private folder this application creates on first run; never serve it as static files. DATA_DIR=./data/languagetool # Required · secret. Generate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env. SECRET_KEY=replace-with-a-random-secret # Required. Use indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured. APP_MODE=indie # Required. The origin used for local forms and links; replace with the chosen HTTPS origin in product mode. SITE_URL=http://127.0.0.1:5000 # Required. The loopback Java server started from the official distribution; Python proxies checks to it. LANGUAGETOOL_URL=http://127.0.0.1:8081
$ choose a build depth, inspect the files, then open the complete pack in your agent
They pay for hosted convenience, premium rules, and cross-device setup.
xpremium rule set
xhosted server convenience
xbrowser extension premium flows
xteam/admin options
xsupport
share on X ↗"I just replaced LanguageTool ($5.47/mo) with one prompt"
Don't feel like building it? These folks already made it free.
HarperA fast English grammar checker that stays on your device and mostly keeps its opinions to itself.open source↗
Writing ToolsSelect text anywhere, hit a hotkey, and fix, rewrite, summarize, or obey a custom instruction without opening another tab.open source↗
WritingToolA LibreOffice extension with long-document checks, style statistics, profiles, and local AI; installing Java is the cover charge.open source↗all 4 free alternatives to LanguageTool →· no votes, no pay-to-list · just what's real
LanguageTool pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| basic | $0 | $0 | 2,000 characters/text on the live comparison page; 3 AI rephrases/day. |
| premium | $24.90 | $5.83 | 150,000 characters/text; unlimited AI paraphrasing. |
| team | — | $5.47/user | Up to 20 users; Premium feature set. |
free tier2,000 characters/text on the live comparison page; 3 AI rephrases/day
billingmonthly + quarterly + annual + 2-year for individual Premium; Team is annual-only
hidden costsTeam pricing is published in EUR; the USD value is an exchange-rate conversion and card/issuer fees or tax can change the final charge.
verified 2026-08-12 · source ↗
Vibecode LanguageTool
Yes. A competent AI coding agent (Claude Code, Codex, Cursor) can build a usable personal LanguageTool replacement in one session with the prompt on this page. It runs on your own machine or server with no subscription.
How much does LanguageTool cost?
LanguageTool costs about $5.47/month (Team, checked 2026-08-12), which is $65.64 per year. That's what you save by replacing it with one prompt.
What do I lose by replacing LanguageTool?
Honestly: premium rule set; hosted server convenience; browser extension premium flows; team/admin options; support. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to LanguageTool?
Yes: Harper (A fast English grammar checker that stays on your device and mostly keeps its opinions to itself.) Writing Tools (Select text anywhere, hit a hotkey, and fix, rewrite, summarize, or obey a custom instruction without opening another tab.) WritingTool (A LibreOffice extension with long-document checks, style statistics, profiles, and local AI; installing Java is the cover charge.) All 4 curated free alternatives are at vibecodeit.com/languagetool/alternatives. The prompt is for when you want it exactly your way.