Vibecode Jobscan
track this build5 phases, 15 steps, beginner friendly0%KINDA · weekend project
The core loop is buildable, but a dependable replacement becomes a real weekend or multi-day project. For Jobscan, compare a resume with a user-supplied job description using transparent term and structure checks. The hard boundary is proprietary ats research, benchmarks, linkedin tools, and job-search workflow, plus data, distribution, and coaching.
the project pack5 files · written for this build, step by step
You are building a lean indie version of Jobscan. 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 ===== # Jobscan · indie build Build a transparent resume-to-job comparison with evidence-based edits. 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. | ## 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. - [ ] **Jobscan 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. ## 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: - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks If one of those is essential to you, that is the reason to keep paying for Jobscan, and the README should say so rather than pretend. ===== BRIEF.md ===== # Build brief · Jobscan 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 transparent resume-to-job comparison with evidence-based edits as a bounded replacement for Jobscan. 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. ### Data model (create this before Phase 1) - resumes(id,title,text) - jobs(id,title,text) - terms(id,job_id,term,weight,source_span) - matches(resume_id,term_id,spans_json) - suggestions(id,resume_id,claim,evidence_span,status) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. ### 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. - Jobscan acceptance sample: Create synthetic sample data before implementation: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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. - 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 text and requirements until these checks pass. ### Phase 2 · Text and requirements Build: Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Extract visible terms with source spans and allow the user to include, exclude or reweight each term. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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 keyword overlap score is not an ATS vendor score or a hiring probability. Done when: Every scored term links to the exact job sentence that introduced it. An empty or image-only input receives a readable error before scoring. Do not build yet: Do not start explainable comparison until these checks pass. ### Phase 3 · Explainable comparison Build: Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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: Substring matching must not treat Java as JavaScript experience. Done when: Repeating Python 100 times does not improve coverage beyond one occurrence. SQL remains missing when no evidence exists in the resume. Do not build yet: Do not start truthful revision workspace until these checks pass. ### Phase 4 · Truthful revision workspace Build: Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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: Never fabricate numbers, degrees, titles or experience to raise a score. Done when: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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 Jobscan replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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) - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow: retain the paid product if this capability is essential; it is outside this bounded build. - proprietary recruiter data: retain the paid product if this capability is essential; it is outside this bounded build. - job-board distribution: retain the paid product if this capability is essential; it is outside this bounded build. - human coaching: retain the paid product if this capability is essential; it is outside this bounded build. - automated application networks: 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. Recheck before upgrading; freeze the versions actually tested. ===== AGENTS.md ===== # Agent instructions · Jobscan 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. 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 keyword overlap score is not an ATS vendor score or a hiring probability. - Substring matching must not treat Java as JavaScript experience. - Never fabricate numbers, degrees, titles or experience to raise a score. - Export without an import or restore rehearsal is not verified portability. ===== BUILD_PLAN.md ===== # Build plan · Jobscan Build a transparent resume-to-job comparison with evidence-based edits. 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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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` ### 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 · Text and requirements Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. ### Steps 1. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Files: `features/text_and_requirements.py`, `templates/text_and_requirements.html` 2. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Files: `features/text_and_requirements.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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_text_and_requirements.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] An empty or image-only input receives a readable error before scoring. ### Watch out - A keyword overlap score is not an ATS vendor score or a hiring probability. ## Phase 3 · Explainable comparison Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. ### Steps 1. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Files: `features/explainable_comparison.py`, `templates/explainable_comparison.html` 2. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Files: `features/explainable_comparison.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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_explainable_comparison.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] SQL remains missing when no evidence exists in the resume. ### Watch out - Substring matching must not treat Java as JavaScript experience. ## Phase 4 · Truthful revision workspace Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. ### Steps 1. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Files: `features/truthful_revision_workspace.py`, `templates/truthful_revision_workspace.html` 2. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Files: `features/truthful_revision_workspace.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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_truthful_revision_workspace.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] Rejecting a suggestion leaves the original resume byte-for-byte unchanged. ### Watch out - Never fabricate numbers, degrees, titles or experience to raise a score. ## Phase 5 · Portable delivery and clean-start proof Finish the Jobscan 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: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks ## After v1, if you want it - Extend truthful revision workspace 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/jobscan # 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
You are building a lean indie version of Jobscan. 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 ===== # Jobscan · indie build Build a transparent resume-to-job comparison with evidence-based edits. 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. | ## 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. - [ ] **Jobscan 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. ## 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: - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks If one of those is essential to you, that is the reason to keep paying for Jobscan, and the README should say so rather than pretend. ===== BRIEF.md ===== # Build brief · Jobscan 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 transparent resume-to-job comparison with evidence-based edits as a bounded replacement for Jobscan. 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. ### Data model (create this before Phase 1) - resumes(id,title,text) - jobs(id,title,text) - terms(id,job_id,term,weight,source_span) - matches(resume_id,term_id,spans_json) - suggestions(id,resume_id,claim,evidence_span,status) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. ### 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. - Jobscan acceptance sample: Create synthetic sample data before implementation: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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. - 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 text and requirements until these checks pass. ### Phase 2 · Text and requirements Build: Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Extract visible terms with source spans and allow the user to include, exclude or reweight each term. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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 keyword overlap score is not an ATS vendor score or a hiring probability. Done when: Every scored term links to the exact job sentence that introduced it. An empty or image-only input receives a readable error before scoring. Do not build yet: Do not start explainable comparison until these checks pass. ### Phase 3 · Explainable comparison Build: Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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: Substring matching must not treat Java as JavaScript experience. Done when: Repeating Python 100 times does not improve coverage beyond one occurrence. SQL remains missing when no evidence exists in the resume. Do not build yet: Do not start truthful revision workspace until these checks pass. ### Phase 4 · Truthful revision workspace Build: Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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: Never fabricate numbers, degrees, titles or experience to raise a score. Done when: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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 Jobscan replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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) - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow: retain the paid product if this capability is essential; it is outside this bounded build. - proprietary recruiter data: retain the paid product if this capability is essential; it is outside this bounded build. - job-board distribution: retain the paid product if this capability is essential; it is outside this bounded build. - human coaching: retain the paid product if this capability is essential; it is outside this bounded build. - automated application networks: 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. Recheck before upgrading; freeze the versions actually tested. ===== AGENTS.md ===== # Agent instructions · Jobscan 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. 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 keyword overlap score is not an ATS vendor score or a hiring probability. - Substring matching must not treat Java as JavaScript experience. - Never fabricate numbers, degrees, titles or experience to raise a score. - Export without an import or restore rehearsal is not verified portability. ===== BUILD_PLAN.md ===== # Build plan · Jobscan Build a transparent resume-to-job comparison with evidence-based edits. 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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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` ### 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 · Text and requirements Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. ### Steps 1. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Files: `features/text_and_requirements.py`, `templates/text_and_requirements.html` 2. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Files: `features/text_and_requirements.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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_text_and_requirements.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] An empty or image-only input receives a readable error before scoring. ### Watch out - A keyword overlap score is not an ATS vendor score or a hiring probability. ## Phase 3 · Explainable comparison Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. ### Steps 1. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Files: `features/explainable_comparison.py`, `templates/explainable_comparison.html` 2. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Files: `features/explainable_comparison.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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_explainable_comparison.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] SQL remains missing when no evidence exists in the resume. ### Watch out - Substring matching must not treat Java as JavaScript experience. ## Phase 4 · Truthful revision workspace Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. ### Steps 1. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Files: `features/truthful_revision_workspace.py`, `templates/truthful_revision_workspace.html` 2. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Files: `features/truthful_revision_workspace.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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_truthful_revision_workspace.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] Rejecting a suggestion leaves the original resume byte-for-byte unchanged. ### Watch out - Never fabricate numbers, degrees, titles or experience to raise a score. ## Phase 5 · Portable delivery and clean-start proof Finish the Jobscan 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: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks ## After v1, if you want it - Extend truthful revision workspace 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/jobscan # 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
You are building a production product version of Jobscan. 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 ===== # Jobscan · product brief ## Problem The core loop is buildable, but a dependable replacement becomes a real weekend or multi-day project. For Jobscan, compare a resume with a user-supplied job description using transparent term and structure checks. The hard boundary is proprietary ats research, benchmarks, linkedin tools, and job-search workflow, plus data, distribution, and coaching. ## Product outcome An operated version of a transparent resume-to-job comparison with evidence-based edits, 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 - local browser - resume and job-description files - optional OpenAI API key ## Explicit non-goals for v1 - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks ## Success criteria - Every scored term links to the exact job sentence that introduced it. - Repeating Python 100 times does not improve coverage beyond one occurrence. - A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - 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 · Jobscan 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 transparent resume-to-job comparison with evidence-based edits as a bounded replacement for Jobscan. 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. ### Data model (create this before Phase 1) - resumes(id,title,text) - jobs(id,title,text) - terms(id,job_id,term,weight,source_span) - matches(resume_id,term_id,spans_json) - suggestions(id,resume_id,claim,evidence_span,status) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. ### 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. - Jobscan acceptance sample: Create synthetic sample data before implementation: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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. - 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 text and requirements until these checks pass. ### Phase 2 · Text and requirements Build: Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Extract visible terms with source spans and allow the user to include, exclude or reweight each term. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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 keyword overlap score is not an ATS vendor score or a hiring probability. Done when: Every scored term links to the exact job sentence that introduced it. An empty or image-only input receives a readable error before scoring. Do not build yet: Do not start explainable comparison until these checks pass. ### Phase 3 · Explainable comparison Build: Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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: Substring matching must not treat Java as JavaScript experience. Done when: Repeating Python 100 times does not improve coverage beyond one occurrence. SQL remains missing when no evidence exists in the resume. Do not build yet: Do not start truthful revision workspace until these checks pass. ### Phase 4 · Truthful revision workspace Build: Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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: Never fabricate numbers, degrees, titles or experience to raise a score. Done when: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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 Jobscan replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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) - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow: retain the paid product if this capability is essential; it is outside this bounded build. - proprietary recruiter data: retain the paid product if this capability is essential; it is outside this bounded build. - job-board distribution: retain the paid product if this capability is essential; it is outside this bounded build. - human coaching: retain the paid product if this capability is essential; it is outside this bounded build. - automated application networks: 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. Recheck before upgrading; freeze the versions actually tested. ===== ARCHITECTURE.md ===== # Architecture · Jobscan ## 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. | ## Modules Each module has one owner concern and a documented way to replace it. | Module | Owns | How to replace it | | --- | --- | --- | | Data and identity | resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status) | Keep stable ids and a versioned repository interface; migrate storage behind it with a fixture round-trip. | | Text and requirements | Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. | Keep the acceptance contract: Every scored term links to the exact job sentence that introduced it. Replace only this module after replaying its fixture; preserve ids and existing records. | | Explainable comparison | Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. | Keep the acceptance contract: Repeating Python 100 times does not improve coverage beyond one occurrence. Replace only this module after replaying its fixture; preserve ids and existing records. | | Truthful revision workspace | Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. | Keep the acceptance contract: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. 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 jobscan. | 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. ## 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 · Jobscan 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. - 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 keyword overlap score is not an ATS vendor score or a hiring probability. - Substring matching must not treat Java as JavaScript experience. - Never fabricate numbers, degrees, titles or experience to raise a score. - 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 · Jobscan 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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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` ### 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 · Text and requirements Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. ### Steps 1. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Files: `features/text_and_requirements.py`, `templates/text_and_requirements.html` 2. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Files: `features/text_and_requirements.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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_text_and_requirements.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] An empty or image-only input receives a readable error before scoring. ### Watch out - A keyword overlap score is not an ATS vendor score or a hiring probability. ## M3 · Explainable comparison Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. ### Steps 1. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Files: `features/explainable_comparison.py`, `templates/explainable_comparison.html` 2. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Files: `features/explainable_comparison.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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_explainable_comparison.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] SQL remains missing when no evidence exists in the resume. ### Watch out - Substring matching must not treat Java as JavaScript experience. ## M4 · Truthful revision workspace Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. ### Steps 1. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Files: `features/truthful_revision_workspace.py`, `templates/truthful_revision_workspace.html` 2. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Files: `features/truthful_revision_workspace.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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_truthful_revision_workspace.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] Rejecting a suggestion leaves the original resume byte-for-byte unchanged. ### Watch out - Never fabricate numbers, degrees, titles or experience to raise a score. ## M5 · Portable delivery and clean-start proof Finish the Jobscan 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: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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 · Jobscan ## 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 jobscan. ## Restore Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 Text and requirements, Explainable comparison, Truthful revision workspace. 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 - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] 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 Jobscan 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/jobscan # 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
README.md
# Jobscan · indie build Build a transparent resume-to-job comparison with evidence-based edits. 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. | ## 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. - [ ] **Jobscan 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. ## 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: - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks If one of those is essential to you, that is the reason to keep paying for Jobscan, and the README should say so rather than pretend.
BRIEF.md
# Build brief · Jobscan 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 transparent resume-to-job comparison with evidence-based edits as a bounded replacement for Jobscan. 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. ### Data model (create this before Phase 1) - resumes(id,title,text) - jobs(id,title,text) - terms(id,job_id,term,weight,source_span) - matches(resume_id,term_id,spans_json) - suggestions(id,resume_id,claim,evidence_span,status) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. ### 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. - Jobscan acceptance sample: Create synthetic sample data before implementation: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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. - 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 text and requirements until these checks pass. ### Phase 2 · Text and requirements Build: Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Extract visible terms with source spans and allow the user to include, exclude or reweight each term. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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 keyword overlap score is not an ATS vendor score or a hiring probability. Done when: Every scored term links to the exact job sentence that introduced it. An empty or image-only input receives a readable error before scoring. Do not build yet: Do not start explainable comparison until these checks pass. ### Phase 3 · Explainable comparison Build: Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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: Substring matching must not treat Java as JavaScript experience. Done when: Repeating Python 100 times does not improve coverage beyond one occurrence. SQL remains missing when no evidence exists in the resume. Do not build yet: Do not start truthful revision workspace until these checks pass. ### Phase 4 · Truthful revision workspace Build: Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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: Never fabricate numbers, degrees, titles or experience to raise a score. Done when: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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 Jobscan replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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) - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow: retain the paid product if this capability is essential; it is outside this bounded build. - proprietary recruiter data: retain the paid product if this capability is essential; it is outside this bounded build. - job-board distribution: retain the paid product if this capability is essential; it is outside this bounded build. - human coaching: retain the paid product if this capability is essential; it is outside this bounded build. - automated application networks: 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. Recheck before upgrading; freeze the versions actually tested.
AGENTS.md
# Agent instructions · Jobscan 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. 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 keyword overlap score is not an ATS vendor score or a hiring probability. - Substring matching must not treat Java as JavaScript experience. - Never fabricate numbers, degrees, titles or experience to raise a score. - Export without an import or restore rehearsal is not verified portability.
BUILD_PLAN.md
# Build plan · Jobscan Build a transparent resume-to-job comparison with evidence-based edits. 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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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` ### 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 · Text and requirements Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. ### Steps 1. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Files: `features/text_and_requirements.py`, `templates/text_and_requirements.html` 2. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Files: `features/text_and_requirements.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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_text_and_requirements.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] An empty or image-only input receives a readable error before scoring. ### Watch out - A keyword overlap score is not an ATS vendor score or a hiring probability. ## Phase 3 · Explainable comparison Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. ### Steps 1. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Files: `features/explainable_comparison.py`, `templates/explainable_comparison.html` 2. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Files: `features/explainable_comparison.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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_explainable_comparison.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] SQL remains missing when no evidence exists in the resume. ### Watch out - Substring matching must not treat Java as JavaScript experience. ## Phase 4 · Truthful revision workspace Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. ### Steps 1. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Files: `features/truthful_revision_workspace.py`, `templates/truthful_revision_workspace.html` 2. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Files: `features/truthful_revision_workspace.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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_truthful_revision_workspace.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] Rejecting a suggestion leaves the original resume byte-for-byte unchanged. ### Watch out - Never fabricate numbers, degrees, titles or experience to raise a score. ## Phase 5 · Portable delivery and clean-start proof Finish the Jobscan 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: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks ## After v1, if you want it - Extend truthful revision workspace 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/jobscan # 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
PRODUCT.md
# Jobscan · product brief ## Problem The core loop is buildable, but a dependable replacement becomes a real weekend or multi-day project. For Jobscan, compare a resume with a user-supplied job description using transparent term and structure checks. The hard boundary is proprietary ats research, benchmarks, linkedin tools, and job-search workflow, plus data, distribution, and coaching. ## Product outcome An operated version of a transparent resume-to-job comparison with evidence-based edits, 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 - local browser - resume and job-description files - optional OpenAI API key ## Explicit non-goals for v1 - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow - proprietary recruiter data - job-board distribution - human coaching - automated application networks ## Success criteria - Every scored term links to the exact job sentence that introduced it. - Repeating Python 100 times does not improve coverage beyond one occurrence. - A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - 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 · Jobscan 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 transparent resume-to-job comparison with evidence-based edits as a bounded replacement for Jobscan. 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. ### Data model (create this before Phase 1) - resumes(id,title,text) - jobs(id,title,text) - terms(id,job_id,term,weight,source_span) - matches(resume_id,term_id,spans_json) - suggestions(id,resume_id,claim,evidence_span,status) - Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them. - Acceptance fixture: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. ### 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. - Jobscan acceptance sample: Create synthetic sample data before implementation: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits. 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. - Configuration: DATA_DIR, SECRET_KEY, APP_MODE, SITE_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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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. - 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 text and requirements until these checks pass. ### Phase 2 · Text and requirements Build: Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. - Extract visible terms with source spans and allow the user to include, exclude or reweight each term. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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 keyword overlap score is not an ATS vendor score or a hiring probability. Done when: Every scored term links to the exact job sentence that introduced it. An empty or image-only input receives a readable error before scoring. Do not build yet: Do not start explainable comparison until these checks pass. ### Phase 3 · Explainable comparison Build: Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. - Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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: Substring matching must not treat Java as JavaScript experience. Done when: Repeating Python 100 times does not improve coverage beyond one occurrence. SQL remains missing when no evidence exists in the resume. Do not build yet: Do not start truthful revision workspace until these checks pass. ### Phase 4 · Truthful revision workspace Build: Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. - Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. - Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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: Never fabricate numbers, degrees, titles or experience to raise a score. Done when: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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 Jobscan replacement with a documented first-run path and a recoverable export. - Document prerequisites, every configuration variable, data locations and the three domain workflows: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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) - A keyword overlap score is not an ATS vendor score or a hiring probability. - This is a transparent resume-to-job comparison with evidence-based edits; full Jobscan feature parity, vendor hosting and support are not included. - proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow: retain the paid product if this capability is essential; it is outside this bounded build. - proprietary recruiter data: retain the paid product if this capability is essential; it is outside this bounded build. - job-board distribution: retain the paid product if this capability is essential; it is outside this bounded build. - human coaching: retain the paid product if this capability is essential; it is outside this bounded build. - automated application networks: 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. Recheck before upgrading; freeze the versions actually tested.
ARCHITECTURE.md
# Architecture · Jobscan ## 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. | ## Modules Each module has one owner concern and a documented way to replace it. | Module | Owns | How to replace it | | --- | --- | --- | | Data and identity | resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status) | Keep stable ids and a versioned repository interface; migrate storage behind it with a fixture round-trip. | | Text and requirements | Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. | Keep the acceptance contract: Every scored term links to the exact job sentence that introduced it. Replace only this module after replaying its fixture; preserve ids and existing records. | | Explainable comparison | Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. | Keep the acceptance contract: Repeating Python 100 times does not improve coverage beyond one occurrence. Replace only this module after replaying its fixture; preserve ids and existing records. | | Truthful revision workspace | Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. | Keep the acceptance contract: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. 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 jobscan. | 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. ## 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 · Jobscan 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. - 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 keyword overlap score is not an ATS vendor score or a hiring probability. - Substring matching must not treat Java as JavaScript experience. - Never fabricate numbers, degrees, titles or experience to raise a score. - 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 · Jobscan 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 transparent resume-to-job comparison with evidence-based edits, 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: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). 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: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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` ### 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 · Text and requirements Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. ### Steps 1. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy. Files: `features/text_and_requirements.py`, `templates/text_and_requirements.html` 2. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Extract visible terms with source spans and allow the user to include, exclude or reweight each term. Files: `features/text_and_requirements.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. 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_text_and_requirements.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] An empty or image-only input receives a readable error before scoring. ### Watch out - A keyword overlap score is not an ATS vendor score or a hiring probability. ## M3 · Explainable comparison Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. ### Steps 1. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match. Files: `features/explainable_comparison.py`, `templates/explainable_comparison.html` 2. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications. Files: `features/explainable_comparison.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. 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_explainable_comparison.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] SQL remains missing when no evidence exists in the resume. ### Watch out - Substring matching must not treat Java as JavaScript experience. ## M4 · Truthful revision workspace Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. ### Steps 1. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting. Files: `features/truthful_revision_workspace.py`, `templates/truthful_revision_workspace.html` 2. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings. Files: `features/truthful_revision_workspace.py` 3. Exercise the acceptance fixture Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. 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_truthful_revision_workspace.py` ```sh python -m unittest discover -s test ``` ### Done when - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] Rejecting a suggestion leaves the original resume byte-for-byte unchanged. ### Watch out - Never fabricate numbers, degrees, titles or experience to raise a score. ## M5 · Portable delivery and clean-start proof Finish the Jobscan 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: Text and requirements; Explainable comparison; Truthful revision workspace. 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 jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 transparent resume-to-job comparison with evidence-based edits 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 jobscan. 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 · Jobscan ## 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 jobscan. ## Restore Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. 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 Text and requirements, Explainable comparison, Truthful revision workspace. 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 - [ ] Every scored term links to the exact job sentence that introduced it. - [ ] Repeating Python 100 times does not improve coverage beyond one occurrence. - [ ] A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. - [ ] 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 Jobscan 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/jobscan # 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
$ choose a build depth, inspect the files, then open the complete pack in your agent
People still pay for Jobscan because people pay for convenience, curated guidance, and distribution; the personal tracking and drafting loop is highly buildable. The recurring cost buys document parsing, truthful claim handling, job-source changes, browser automation rules, privacy, model drift, and user review, not just the visible interface.
xproprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow
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xjob-board distribution
xhuman coaching
xautomated application networks
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Jobscan pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0/user | $0/user | 5 resume scans per month; unlimited resume-builder downloads. |
| premium monthly | $49.95/user | — | Unlimited resume scans and the full Premium optimization toolkit. |
| premium quarterly | $29.98/user | — | Unlimited resume scans and the full Premium optimization toolkit. |
free tier5 resume scans per month and unlimited resume-builder downloads
billingmonthly + quarterly; no annual plan; auto-renewing
hidden costsThe $29.98/month headline is a quarterly equivalent requiring $89.95 upfront; the monthly plan is $49.95 and does not carry the same 7-day trial disclosure.
verified 2026-08-14 · source ↗
Vibecode Jobscan
Kinda. The core of Jobscan is buildable in a weekend with the prompt on this page, but there are real gaps: proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow, proprietary recruiter data. Read the honest list above before committing.
How much does Jobscan cost?
Jobscan costs about $49.95/month (Premium, checked 2026-07-31), which is $599.4000000000001 per year.
What do I lose by replacing Jobscan?
Honestly: proprietary ATS research, benchmarks, LinkedIn tools, and job-search workflow; proprietary recruiter data; job-board distribution; human coaching; automated application networks. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Jobscan?
Yes: Resume Matcher (Paste a job description, inspect the keyword and section gaps, then generate a truthful tailored copy.) Jobalytics ATS Checker (A genuinely unlimited checker for matched and missing terms, without making you create an account first.) The prompt is for when you want it exactly your way.