Vibecode OpenPTE
track this build5 steps, step by step0%KINDA · weekend project
The mechanics of a PTE trainer are not hard: record audio, transcribe it, time the task, score against a rubric, keep a history. An agent can build that in a weekend using Whisper for transcription and an LLM for rubric feedback, and you will genuinely practice more because the loop is yours. What you cannot build is the part people actually pay for: a question bank that tracks what is currently showing up in the real exam, and a scoring model calibrated against Pearson's automated marker so the number you see means something. Your DIY grader will be directionally useful and numerically fictional. Good enough for drilling fluency and essay structure, not good enough to decide whether you are ready to book the test.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of OpenPTE. 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 ===== # OpenPTE indie build ## Goal Build the smallest trustworthy replacement for the core OpenPTE workflow for one developer or a tiny team. ## Scope A local web app that runs timed PTE-style tasks, records your speaking or captures your typing, transcribes it, and returns rubric feedback plus a trend chart across attempts. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Scores calibrated to the real automated marker, so your numbers are vibes, not predictions - A question bank that is maintained and rotated as the exam changes - Full mock tests with official section timing, weighting and score report layout - Model answers, templates and community discussion around each item - Mobile apps, cross-device sync, and anyone to blame when the grader is wrong If those capabilities are essential, use OpenPTE instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local PTE Academic practice trainer. Empty folder, no accounts, no telemetry, no cloud database. Stack, non negotiable: Next.js 15 with the App Router, TypeScript, Tailwind, and SQLite via better-sqlite3. Everything runs with `npm run dev` on localhost. Secrets in .env.local only, read `OPENAI_API_KEY` and `OPENAI_BASE_URL`. Data: a `questions/` folder of JSON files, one per task type, each item having id, type, prompt text, optional imagePath, optional audioPath, timeLimitSeconds, and preparationSeconds. Ship 3 dummy items per type so the app runs before the user adds real content. Do not scrape or invent exam content. Task types to support: Read Aloud, Repeat Sentence, Describe Image, Retell Lecture (speaking, recorded), plus Summarize Written Text, Write Essay (typed), and Reading Fill in the Blanks (drag or select). Practice flow: pick a task type, get a random unattempted item, show preparation countdown, then the recording or typing window with a hard timer that auto-submits. Use MediaRecorder for audio, store the webm blob under `data/recordings/`. Scoring: for speaking, send audio to the Whisper transcription endpoint, then compute words per minute, filled pause count, and for Read Aloud a word level diff against the reference text. For writing, compute word count and run a rubric prompt. In both cases call the chat model with a task specific rubric that returns strict JSON: content, form, fluency, pronunciation or grammar, each 0 to 5, plus two sentences of feedback and three concrete fixes. Store the raw JSON. Label every score in the UI as "unofficial, uncalibrated" and never render a 10 to 90 style score. This is deliberate. History: an attempts table plus a dashboard with a per task type trend line, a list of past attempts with playback of the stored audio, and a CSV export. Out of scope: full mock tests, official score mapping, user accounts, payments, mobile apps, sync, and any bundled question bank. Deliver a README covering setup, how to add your own items to `questions/`, and a blunt paragraph on why these scores are not predictions. ## Required capabilities - Node 20 and a modern browser with mic permission - An OpenAI-compatible API key in .env for transcription and rubric scoring, or a local whisper.cpp build - Your own question set: prompts, images, and audio you supply as JSON - Time to write the rubric prompts per task type, which is most of the actual work ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of OpenPTE. 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 ===== # OpenPTE indie build ## Goal Build the smallest trustworthy replacement for the core OpenPTE workflow for one developer or a tiny team. ## Scope A local web app that runs timed PTE-style tasks, records your speaking or captures your typing, transcribes it, and returns rubric feedback plus a trend chart across attempts. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Scores calibrated to the real automated marker, so your numbers are vibes, not predictions - A question bank that is maintained and rotated as the exam changes - Full mock tests with official section timing, weighting and score report layout - Model answers, templates and community discussion around each item - Mobile apps, cross-device sync, and anyone to blame when the grader is wrong If those capabilities are essential, use OpenPTE instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local PTE Academic practice trainer. Empty folder, no accounts, no telemetry, no cloud database. Stack, non negotiable: Next.js 15 with the App Router, TypeScript, Tailwind, and SQLite via better-sqlite3. Everything runs with `npm run dev` on localhost. Secrets in .env.local only, read `OPENAI_API_KEY` and `OPENAI_BASE_URL`. Data: a `questions/` folder of JSON files, one per task type, each item having id, type, prompt text, optional imagePath, optional audioPath, timeLimitSeconds, and preparationSeconds. Ship 3 dummy items per type so the app runs before the user adds real content. Do not scrape or invent exam content. Task types to support: Read Aloud, Repeat Sentence, Describe Image, Retell Lecture (speaking, recorded), plus Summarize Written Text, Write Essay (typed), and Reading Fill in the Blanks (drag or select). Practice flow: pick a task type, get a random unattempted item, show preparation countdown, then the recording or typing window with a hard timer that auto-submits. Use MediaRecorder for audio, store the webm blob under `data/recordings/`. Scoring: for speaking, send audio to the Whisper transcription endpoint, then compute words per minute, filled pause count, and for Read Aloud a word level diff against the reference text. For writing, compute word count and run a rubric prompt. In both cases call the chat model with a task specific rubric that returns strict JSON: content, form, fluency, pronunciation or grammar, each 0 to 5, plus two sentences of feedback and three concrete fixes. Store the raw JSON. Label every score in the UI as "unofficial, uncalibrated" and never render a 10 to 90 style score. This is deliberate. History: an attempts table plus a dashboard with a per task type trend line, a list of past attempts with playback of the stored audio, and a CSV export. Out of scope: full mock tests, official score mapping, user accounts, payments, mobile apps, sync, and any bundled question bank. Deliver a README covering setup, how to add your own items to `questions/`, and a blunt paragraph on why these scores are not predictions. ## Required capabilities - Node 20 and a modern browser with mic permission - An OpenAI-compatible API key in .env for transcription and rubric scoring, or a local whisper.cpp build - Your own question set: prompts, images, and audio you supply as JSON - Time to write the rubric prompts per task type, which is most of the actual work ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of OpenPTE. 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 ===== # OpenPTE product brief ## Problem The mechanics of a PTE trainer are not hard: record audio, transcribe it, time the task, score against a rubric, keep a history. An agent can build that in a weekend using Whisper for transcription and an LLM for rubric feedback, and you will genuinely practice more because the loop is yours. What you cannot build is the part people actually pay for: a question bank that tracks what is currently showing up in the real exam, and a scoring model calibrated against Pearson's automated marker so the number you see means something. Your DIY grader will be directionally useful and numerically fictional. Good enough for drilling fluency and essay structure, not good enough to decide whether you are ready to book the test. ## Product outcome A local web app that runs timed PTE-style tasks, records your speaking or captures your typing, transcribes it, and returns rubric feedback plus a trend chart across attempts. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Node 20 and a modern browser with mic permission - An OpenAI-compatible API key in .env for transcription and rubric scoring, or a local whisper.cpp build - Your own question set: prompts, images, and audio you supply as JSON - Time to write the rubric prompts per task type, which is most of the actual work ## Explicit non-goals for v1 - Scores calibrated to the real automated marker, so your numbers are vibes, not predictions - A question bank that is maintained and rotated as the exam changes - Full mock tests with official section timing, weighting and score report layout - Model answers, templates and community discussion around each item - Mobile apps, cross-device sync, and anyone to blame when the grader is wrong ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a local PTE Academic practice trainer. Empty folder, no accounts, no telemetry, no cloud database. Stack, non negotiable: Next.js 15 with the App Router, TypeScript, Tailwind, and SQLite via better-sqlite3. Everything runs with `npm run dev` on localhost. Secrets in .env.local only, read `OPENAI_API_KEY` and `OPENAI_BASE_URL`. Data: a `questions/` folder of JSON files, one per task type, each item having id, type, prompt text, optional imagePath, optional audioPath, timeLimitSeconds, and preparationSeconds. Ship 3 dummy items per type so the app runs before the user adds real content. Do not scrape or invent exam content. Task types to support: Read Aloud, Repeat Sentence, Describe Image, Retell Lecture (speaking, recorded), plus Summarize Written Text, Write Essay (typed), and Reading Fill in the Blanks (drag or select). Practice flow: pick a task type, get a random unattempted item, show preparation countdown, then the recording or typing window with a hard timer that auto-submits. Use MediaRecorder for audio, store the webm blob under `data/recordings/`. Scoring: for speaking, send audio to the Whisper transcription endpoint, then compute words per minute, filled pause count, and for Read Aloud a word level diff against the reference text. For writing, compute word count and run a rubric prompt. In both cases call the chat model with a task specific rubric that returns strict JSON: content, form, fluency, pronunciation or grammar, each 0 to 5, plus two sentences of feedback and three concrete fixes. Store the raw JSON. Label every score in the UI as "unofficial, uncalibrated" and never render a 10 to 90 style score. This is deliberate. History: an attempts table plus a dashboard with a per task type trend line, a list of past attempts with playback of the stored audio, and a CSV export. Out of scope: full mock tests, official score mapping, user accounts, payments, mobile apps, sync, and any bundled question bank. Deliver a README covering setup, how to add your own items to `questions/`, and a blunt paragraph on why these scores are not predictions. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - 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 test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 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 source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted OpenPTE capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# OpenPTE indie build ## Goal Build the smallest trustworthy replacement for the core OpenPTE workflow for one developer or a tiny team. ## Scope A local web app that runs timed PTE-style tasks, records your speaking or captures your typing, transcribes it, and returns rubric feedback plus a trend chart across attempts. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Scores calibrated to the real automated marker, so your numbers are vibes, not predictions - A question bank that is maintained and rotated as the exam changes - Full mock tests with official section timing, weighting and score report layout - Model answers, templates and community discussion around each item - Mobile apps, cross-device sync, and anyone to blame when the grader is wrong If those capabilities are essential, use OpenPTE instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a local PTE Academic practice trainer. Empty folder, no accounts, no telemetry, no cloud database. Stack, non negotiable: Next.js 15 with the App Router, TypeScript, Tailwind, and SQLite via better-sqlite3. Everything runs with `npm run dev` on localhost. Secrets in .env.local only, read `OPENAI_API_KEY` and `OPENAI_BASE_URL`. Data: a `questions/` folder of JSON files, one per task type, each item having id, type, prompt text, optional imagePath, optional audioPath, timeLimitSeconds, and preparationSeconds. Ship 3 dummy items per type so the app runs before the user adds real content. Do not scrape or invent exam content. Task types to support: Read Aloud, Repeat Sentence, Describe Image, Retell Lecture (speaking, recorded), plus Summarize Written Text, Write Essay (typed), and Reading Fill in the Blanks (drag or select). Practice flow: pick a task type, get a random unattempted item, show preparation countdown, then the recording or typing window with a hard timer that auto-submits. Use MediaRecorder for audio, store the webm blob under `data/recordings/`. Scoring: for speaking, send audio to the Whisper transcription endpoint, then compute words per minute, filled pause count, and for Read Aloud a word level diff against the reference text. For writing, compute word count and run a rubric prompt. In both cases call the chat model with a task specific rubric that returns strict JSON: content, form, fluency, pronunciation or grammar, each 0 to 5, plus two sentences of feedback and three concrete fixes. Store the raw JSON. Label every score in the UI as "unofficial, uncalibrated" and never render a 10 to 90 style score. This is deliberate. History: an attempts table plus a dashboard with a per task type trend line, a list of past attempts with playback of the stored audio, and a CSV export. Out of scope: full mock tests, official score mapping, user accounts, payments, mobile apps, sync, and any bundled question bank. Deliver a README covering setup, how to add your own items to `questions/`, and a blunt paragraph on why these scores are not predictions. ## Required capabilities - Node 20 and a modern browser with mic permission - An OpenAI-compatible API key in .env for transcription and rubric scoring, or a local whisper.cpp build - Your own question set: prompts, images, and audio you supply as JSON - Time to write the rubric prompts per task type, which is most of the actual work ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# OpenPTE product brief ## Problem The mechanics of a PTE trainer are not hard: record audio, transcribe it, time the task, score against a rubric, keep a history. An agent can build that in a weekend using Whisper for transcription and an LLM for rubric feedback, and you will genuinely practice more because the loop is yours. What you cannot build is the part people actually pay for: a question bank that tracks what is currently showing up in the real exam, and a scoring model calibrated against Pearson's automated marker so the number you see means something. Your DIY grader will be directionally useful and numerically fictional. Good enough for drilling fluency and essay structure, not good enough to decide whether you are ready to book the test. ## Product outcome A local web app that runs timed PTE-style tasks, records your speaking or captures your typing, transcribes it, and returns rubric feedback plus a trend chart across attempts. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Node 20 and a modern browser with mic permission - An OpenAI-compatible API key in .env for transcription and rubric scoring, or a local whisper.cpp build - Your own question set: prompts, images, and audio you supply as JSON - Time to write the rubric prompts per task type, which is most of the actual work ## Explicit non-goals for v1 - Scores calibrated to the real automated marker, so your numbers are vibes, not predictions - A question bank that is maintained and rotated as the exam changes - Full mock tests with official section timing, weighting and score report layout - Model answers, templates and community discussion around each item - Mobile apps, cross-device sync, and anyone to blame when the grader is wrong ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a local PTE Academic practice trainer. Empty folder, no accounts, no telemetry, no cloud database. Stack, non negotiable: Next.js 15 with the App Router, TypeScript, Tailwind, and SQLite via better-sqlite3. Everything runs with `npm run dev` on localhost. Secrets in .env.local only, read `OPENAI_API_KEY` and `OPENAI_BASE_URL`. Data: a `questions/` folder of JSON files, one per task type, each item having id, type, prompt text, optional imagePath, optional audioPath, timeLimitSeconds, and preparationSeconds. Ship 3 dummy items per type so the app runs before the user adds real content. Do not scrape or invent exam content. Task types to support: Read Aloud, Repeat Sentence, Describe Image, Retell Lecture (speaking, recorded), plus Summarize Written Text, Write Essay (typed), and Reading Fill in the Blanks (drag or select). Practice flow: pick a task type, get a random unattempted item, show preparation countdown, then the recording or typing window with a hard timer that auto-submits. Use MediaRecorder for audio, store the webm blob under `data/recordings/`. Scoring: for speaking, send audio to the Whisper transcription endpoint, then compute words per minute, filled pause count, and for Read Aloud a word level diff against the reference text. For writing, compute word count and run a rubric prompt. In both cases call the chat model with a task specific rubric that returns strict JSON: content, form, fluency, pronunciation or grammar, each 0 to 5, plus two sentences of feedback and three concrete fixes. Store the raw JSON. Label every score in the UI as "unofficial, uncalibrated" and never render a 10 to 90 style score. This is deliberate. History: an attempts table plus a dashboard with a per task type trend line, a list of past attempts with playback of the stored audio, and a CSV export. Out of scope: full mock tests, official score mapping, user accounts, payments, mobile apps, sync, and any bundled question bank. Deliver a README covering setup, how to add your own items to `questions/`, and a blunt paragraph on why these scores are not predictions. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - 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 test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 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 source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted OpenPTE capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
Because a test taker is not buying software, they are buying a number they can trust before they pay the exam fee. A prep platform's value is the item bank that mirrors what is currently in circulation and a grader tuned so a 79 on the practice test means roughly a 79 on the day. Both of those are accumulated data work, not code. A self-built trainer is great for volume practice and terrible for readiness signals, which is exactly the wrong half to have if you only get one shot at the visa cutoff.
xScores calibrated to the real automated marker, so your numbers are vibes, not predictions
xA question bank that is maintained and rotated as the exam changes
xFull mock tests with official section timing, weighting and score report layout
xModel answers, templates and community discussion around each item
xMobile apps, cross-device sync, and anyone to blame when the grader is wrong
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced OpenPTE ($17.99/mo) with one prompt"
Vibecode OpenPTE
Kinda. The core of OpenPTE is buildable in a weekend with the prompt on this page, but there are real gaps: Scores calibrated to the real automated marker, so your numbers are vibes, not predictions, A question bank that is maintained and rotated as the exam changes. Read the honest list above before committing.
How much does OpenPTE cost?
OpenPTE costs about $17.99/month (Premium 30-day pass, checked 2026-08-18), which is $215.88 per year.
What do I lose by replacing OpenPTE?
Honestly: Scores calibrated to the real automated marker, so your numbers are vibes, not predictions; A question bank that is maintained and rotated as the exam changes; Full mock tests with official section timing, weighting and score report layout; Model answers, templates and community discussion around each item; Mobile apps, cross-device sync, and anyone to blame when the grader is wrong. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to OpenPTE?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.