Vibecode TranscriptAPI

You are building a lean indie version of TranscriptAPI.

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 =====
# TranscriptAPI indie build

## Goal

Build the smallest trustworthy replacement for the core TranscriptAPI workflow for one developer or a tiny team.

## Scope

A FastAPI endpoint that takes a YouTube URL and returns the caption track as timestamped JSON.

## 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:
- staying unblocked when YouTube rate-limits caption fetches
- search, channel and playlist endpoints
- reliability at bulk volume
- an SLA and support when YouTube changes something

If those capabilities are essential, use youtube-transcript-api 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 me a small YouTube transcript API service, a personal stand-in for TranscriptAPI.
Requirements:
- Python 3.11 stack: FastAPI, uvicorn, and the youtube-transcript-api library; no
  database, one file plus a README is fine.
- GET /transcript?video=<id-or-url>: extract the video id, fetch the caption track,
  return JSON with the full text plus timestamped segments.
- Support lang= with a sensible fallback to the first available track, and report
  which track was actually used in the response.
- Clean JSON errors: 404 when captions are disabled or missing, 502 when YouTube
  blocks the fetch; never a bare 500.
- Read an optional comma-separated PROXIES var from .env and rotate per request;
  with none set, run direct.
- GET /health returning version and uptime.
- A tiny CLI example in the README: curl the endpoint, jq out the text.
- Keep it stateless and private: no accounts, no API keys, no telemetry, no storage.
- README must be honest about the failure mode: at any real volume YouTube
  rate-limits and IP-blocks caption fetches, and this build has no defense.
- Out of scope, deliberately: the rotating proxy pool and anti-blocking
  infrastructure, channel/playlist/search endpoints, bulk throughput, and SLAs.
  That reliability layer is the thing the paid product actually sells.

## Required capabilities

- Python 3.11
- a rotating proxy pool if you use it at any volume

## 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 TranscriptAPI.

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 =====
# TranscriptAPI indie build

## Goal

Build the smallest trustworthy replacement for the core TranscriptAPI workflow for one developer or a tiny team.

## Scope

A FastAPI endpoint that takes a YouTube URL and returns the caption track as timestamped JSON.

## 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:
- staying unblocked when YouTube rate-limits caption fetches
- search, channel and playlist endpoints
- reliability at bulk volume
- an SLA and support when YouTube changes something

If those capabilities are essential, use youtube-transcript-api 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 me a small YouTube transcript API service, a personal stand-in for TranscriptAPI.
Requirements:
- Python 3.11 stack: FastAPI, uvicorn, and the youtube-transcript-api library; no
  database, one file plus a README is fine.
- GET /transcript?video=<id-or-url>: extract the video id, fetch the caption track,
  return JSON with the full text plus timestamped segments.
- Support lang= with a sensible fallback to the first available track, and report
  which track was actually used in the response.
- Clean JSON errors: 404 when captions are disabled or missing, 502 when YouTube
  blocks the fetch; never a bare 500.
- Read an optional comma-separated PROXIES var from .env and rotate per request;
  with none set, run direct.
- GET /health returning version and uptime.
- A tiny CLI example in the README: curl the endpoint, jq out the text.
- Keep it stateless and private: no accounts, no API keys, no telemetry, no storage.
- README must be honest about the failure mode: at any real volume YouTube
  rate-limits and IP-blocks caption fetches, and this build has no defense.
- Out of scope, deliberately: the rotating proxy pool and anti-blocking
  infrastructure, channel/playlist/search endpoints, bulk throughput, and SLAs.
  That reliability layer is the thing the paid product actually sells.

## Required capabilities

- Python 3.11
- a rotating proxy pool if you use it at any volume

## 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 TranscriptAPI.

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 =====
# TranscriptAPI product brief

## Problem

The happy path is genuinely a one-sitting build: the open-source youtube-transcript-api library fetches a caption track in a few lines, and wrapping it in FastAPI gives you a working personal endpoint. The honest gap is reliability. YouTube rate-limits and IP-blocks caption scraping at any real volume, so the DIY version works until it suddenly does not, and there is no fix without a rotating proxy pool you must rent and operate. The paid product is not selling the parsing; it is selling the unblocked pipe, plus search and playlist endpoints on top.

## Product outcome

A FastAPI endpoint that takes a YouTube URL and returns the caption track as timestamped JSON.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- Python 3.11
- a rotating proxy pool if you use it at any volume

## Explicit non-goals for v1

- staying unblocked when YouTube rate-limits caption fetches
- search, channel and playlist endpoints
- reliability at bulk volume
- an SLA and support when YouTube changes something

## 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 me a small YouTube transcript API service, a personal stand-in for TranscriptAPI.
Requirements:
- Python 3.11 stack: FastAPI, uvicorn, and the youtube-transcript-api library; no
  database, one file plus a README is fine.
- GET /transcript?video=<id-or-url>: extract the video id, fetch the caption track,
  return JSON with the full text plus timestamped segments.
- Support lang= with a sensible fallback to the first available track, and report
  which track was actually used in the response.
- Clean JSON errors: 404 when captions are disabled or missing, 502 when YouTube
  blocks the fetch; never a bare 500.
- Read an optional comma-separated PROXIES var from .env and rotate per request;
  with none set, run direct.
- GET /health returning version and uptime.
- A tiny CLI example in the README: curl the endpoint, jq out the text.
- Keep it stateless and private: no accounts, no API keys, no telemetry, no storage.
- README must be honest about the failure mode: at any real volume YouTube
  rate-limits and IP-blocks caption fetches, and this build has no defense.
- Out of scope, deliberately: the rotating proxy pool and anti-blocking
  infrastructure, channel/playlist/search endpoints, bulk throughput, and SLAs.
  That reliability layer is the thing the paid product actually sells.

## 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 TranscriptAPI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# TranscriptAPI indie build

## Goal

Build the smallest trustworthy replacement for the core TranscriptAPI workflow for one developer or a tiny team.

## Scope

A FastAPI endpoint that takes a YouTube URL and returns the caption track as timestamped JSON.

## 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:
- staying unblocked when YouTube rate-limits caption fetches
- search, channel and playlist endpoints
- reliability at bulk volume
- an SLA and support when YouTube changes something

If those capabilities are essential, use youtube-transcript-api instead of pretending the gap is solved.

# 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

## Original build brief

Build me a small YouTube transcript API service, a personal stand-in for TranscriptAPI.
Requirements:
- Python 3.11 stack: FastAPI, uvicorn, and the youtube-transcript-api library; no
  database, one file plus a README is fine.
- GET /transcript?video=<id-or-url>: extract the video id, fetch the caption track,
  return JSON with the full text plus timestamped segments.
- Support lang= with a sensible fallback to the first available track, and report
  which track was actually used in the response.
- Clean JSON errors: 404 when captions are disabled or missing, 502 when YouTube
  blocks the fetch; never a bare 500.
- Read an optional comma-separated PROXIES var from .env and rotate per request;
  with none set, run direct.
- GET /health returning version and uptime.
- A tiny CLI example in the README: curl the endpoint, jq out the text.
- Keep it stateless and private: no accounts, no API keys, no telemetry, no storage.
- README must be honest about the failure mode: at any real volume YouTube
  rate-limits and IP-blocks caption fetches, and this build has no defense.
- Out of scope, deliberately: the rotating proxy pool and anti-blocking
  infrastructure, channel/playlist/search endpoints, bulk throughput, and SLAs.
  That reliability layer is the thing the paid product actually sells.

## Required capabilities

- Python 3.11
- a rotating proxy pool if you use it at any volume

## 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.

# 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.

# TranscriptAPI product brief

## Problem

The happy path is genuinely a one-sitting build: the open-source youtube-transcript-api library fetches a caption track in a few lines, and wrapping it in FastAPI gives you a working personal endpoint. The honest gap is reliability. YouTube rate-limits and IP-blocks caption scraping at any real volume, so the DIY version works until it suddenly does not, and there is no fix without a rotating proxy pool you must rent and operate. The paid product is not selling the parsing; it is selling the unblocked pipe, plus search and playlist endpoints on top.

## Product outcome

A FastAPI endpoint that takes a YouTube URL and returns the caption track as timestamped JSON.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- Python 3.11
- a rotating proxy pool if you use it at any volume

## Explicit non-goals for v1

- staying unblocked when YouTube rate-limits caption fetches
- search, channel and playlist endpoints
- reliability at bulk volume
- an SLA and support when YouTube changes something

## 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

## Starting brief

Build me a small YouTube transcript API service, a personal stand-in for TranscriptAPI.
Requirements:
- Python 3.11 stack: FastAPI, uvicorn, and the youtube-transcript-api library; no
  database, one file plus a README is fine.
- GET /transcript?video=<id-or-url>: extract the video id, fetch the caption track,
  return JSON with the full text plus timestamped segments.
- Support lang= with a sensible fallback to the first available track, and report
  which track was actually used in the response.
- Clean JSON errors: 404 when captions are disabled or missing, 502 when YouTube
  blocks the fetch; never a bare 500.
- Read an optional comma-separated PROXIES var from .env and rotate per request;
  with none set, run direct.
- GET /health returning version and uptime.
- A tiny CLI example in the README: curl the endpoint, jq out the text.
- Keep it stateless and private: no accounts, no API keys, no telemetry, no storage.
- README must be honest about the failure mode: at any real volume YouTube
  rate-limits and IP-blocks caption fetches, and this build has no defense.
- Out of scope, deliberately: the rotating proxy pool and anti-blocking
  infrastructure, channel/playlist/search endpoints, bulk throughput, and SLAs.
  That reliability layer is the thing the paid product actually sells.

## 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.

# 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.

# 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

## 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 TranscriptAPI 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