Vibecode Greptile

You are building a lean indie version of Greptile.

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 =====
# Greptile indie build

## Goal

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

## Scope

A GitHub App that on each pull request retrieves semantically related code from a local embedding index of the repo and posts LLM-written inline review comments on the diff.

## 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:
- Tuned false-positive suppression: their bot has been beaten into silence by thousands of teams, yours has not
- Incremental reindexing and monorepo handling that does not choke on a 500k file tree
- Memory of past reviews so the same nit is not raised on every force push
- Team-level config, custom rule sets, and per-repo style learning from accepted or dismissed comments
- Bitbucket, GitLab, and self-hosted host support, plus SOC 2 paperwork your security team will ask for

If those capabilities are essential, use Greptile 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 self-hosted AI pull request reviewer for a single GitHub repository. No web UI, no accounts, no telemetry.

Stack, no substitutions: Python 3.12, FastAPI, uvicorn, SQLite for state, sqlite-vec for vector search, httpx for GitHub REST calls, OpenAI API for embeddings and review generation. Package with uv. Everything runs in one process plus one CLI.

Secrets in .env, loaded with python-dotenv: GITHUB_APP_ID, GITHUB_PRIVATE_KEY_PATH, GITHUB_WEBHOOK_SECRET, OPENAI_API_KEY, REPO_FULL_NAME.

Part 1, indexer CLI (index.py):
- Walk the local clone of the repo, respect .gitignore, skip binaries, lockfiles, and anything over 400 KB.
- Chunk source files by function or class using tree-sitter for Python, TypeScript, JavaScript, and Go; fall back to 60-line sliding windows with 10-line overlap for everything else.
- Embed each chunk, store text, path, start line, end line, git blob sha, and vector in SQLite.
- Support incremental reindex: given two git revisions, only re-embed chunks whose file blob sha changed. Print counts of added, updated, deleted chunks.

Part 2, webhook service (server.py):
- POST /webhook, verify the X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET, reject on mismatch.
- Handle pull_request opened and synchronize events only. Enqueue work in a SQLite-backed job table and return 202 immediately.
- A background worker fetches the PR diff, splits it per file hunk, and for each hunk retrieves the top 8 related chunks by vector similarity plus the full current version of the changed file if it is under 800 lines.
- Send one LLM call per changed file with the hunk, retrieved context, and a review prompt that demands: only comment on correctness, security, or clear API misuse; no style nits; no praise; no summaries; return an empty array when the change is fine.
- Model output must be strict JSON: a list of objects with path, line, severity, body. Validate with Pydantic and drop anything whose line is not inside the diff.

Part 3, dedupe and posting:
- Store a hash of path plus normalized comment body per PR. Never post the same finding twice across force pushes.
- Post surviving comments as a single GitHub review with inline comments, authenticated as the GitHub App via a short-lived installation token.
- Add a CLI command 'review-local PR_NUMBER' that prints what it would post without calling GitHub, for prompt tuning.

Out of scope: GitLab and Bitbucket, multi-repo support, a dashboard, learning from dismissed comments, autofix suggestions, chat replies to review threads.

Deliver a README with GitHub App setup steps, the exact permissions needed (contents read, pull requests write, metadata read), and how to expose the webhook locally with a tunnel. Include pytest coverage for signature verification, diff line mapping, and dedupe.

## Required capabilities

- GitHub App registration with webhook secret and private key
- An LLM API key (embeddings plus a strong reasoning model)
- A small always-on host or tunnel to receive webhooks
- Local disk for the vector index, roughly proportional to repo size
- Willingness to iterate on the review prompt for weeks

## 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 Greptile.

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 =====
# Greptile indie build

## Goal

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

## Scope

A GitHub App that on each pull request retrieves semantically related code from a local embedding index of the repo and posts LLM-written inline review comments on the diff.

## 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:
- Tuned false-positive suppression: their bot has been beaten into silence by thousands of teams, yours has not
- Incremental reindexing and monorepo handling that does not choke on a 500k file tree
- Memory of past reviews so the same nit is not raised on every force push
- Team-level config, custom rule sets, and per-repo style learning from accepted or dismissed comments
- Bitbucket, GitLab, and self-hosted host support, plus SOC 2 paperwork your security team will ask for

If those capabilities are essential, use Greptile 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 self-hosted AI pull request reviewer for a single GitHub repository. No web UI, no accounts, no telemetry.

Stack, no substitutions: Python 3.12, FastAPI, uvicorn, SQLite for state, sqlite-vec for vector search, httpx for GitHub REST calls, OpenAI API for embeddings and review generation. Package with uv. Everything runs in one process plus one CLI.

Secrets in .env, loaded with python-dotenv: GITHUB_APP_ID, GITHUB_PRIVATE_KEY_PATH, GITHUB_WEBHOOK_SECRET, OPENAI_API_KEY, REPO_FULL_NAME.

Part 1, indexer CLI (index.py):
- Walk the local clone of the repo, respect .gitignore, skip binaries, lockfiles, and anything over 400 KB.
- Chunk source files by function or class using tree-sitter for Python, TypeScript, JavaScript, and Go; fall back to 60-line sliding windows with 10-line overlap for everything else.
- Embed each chunk, store text, path, start line, end line, git blob sha, and vector in SQLite.
- Support incremental reindex: given two git revisions, only re-embed chunks whose file blob sha changed. Print counts of added, updated, deleted chunks.

Part 2, webhook service (server.py):
- POST /webhook, verify the X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET, reject on mismatch.
- Handle pull_request opened and synchronize events only. Enqueue work in a SQLite-backed job table and return 202 immediately.
- A background worker fetches the PR diff, splits it per file hunk, and for each hunk retrieves the top 8 related chunks by vector similarity plus the full current version of the changed file if it is under 800 lines.
- Send one LLM call per changed file with the hunk, retrieved context, and a review prompt that demands: only comment on correctness, security, or clear API misuse; no style nits; no praise; no summaries; return an empty array when the change is fine.
- Model output must be strict JSON: a list of objects with path, line, severity, body. Validate with Pydantic and drop anything whose line is not inside the diff.

Part 3, dedupe and posting:
- Store a hash of path plus normalized comment body per PR. Never post the same finding twice across force pushes.
- Post surviving comments as a single GitHub review with inline comments, authenticated as the GitHub App via a short-lived installation token.
- Add a CLI command 'review-local PR_NUMBER' that prints what it would post without calling GitHub, for prompt tuning.

Out of scope: GitLab and Bitbucket, multi-repo support, a dashboard, learning from dismissed comments, autofix suggestions, chat replies to review threads.

Deliver a README with GitHub App setup steps, the exact permissions needed (contents read, pull requests write, metadata read), and how to expose the webhook locally with a tunnel. Include pytest coverage for signature verification, diff line mapping, and dedupe.

## Required capabilities

- GitHub App registration with webhook secret and private key
- An LLM API key (embeddings plus a strong reasoning model)
- A small always-on host or tunnel to receive webhooks
- Local disk for the vector index, roughly proportional to repo size
- Willingness to iterate on the review prompt for weeks

## 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 Greptile.

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 =====
# Greptile product brief

## Problem

The mechanical core is genuinely a weekend project: a GitHub App that catches pull request webhooks, pulls the diff, retrieves related code from an embedding index of the repo, and posts inline comments from an LLM. You can get to first useful comment in an afternoon. What you will not get in one sitting is signal quality, which is the entire product: knowing when to shut up, not re-flagging the same nit on every push, understanding a monorepo without blowing the context window, and keeping the index fresh without a full reindex on every merge. Expect a bot that is impressive on day one and muted by the team on day nine. Worth building if you own the repo and enjoy tuning prompts; not worth building to save a per-seat fee across a real engineering org.

## Product outcome

A GitHub App that on each pull request retrieves semantically related code from a local embedding index of the repo and posts LLM-written inline review comments on the diff.

## Target user

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

## Required capabilities

- GitHub App registration with webhook secret and private key
- An LLM API key (embeddings plus a strong reasoning model)
- A small always-on host or tunnel to receive webhooks
- Local disk for the vector index, roughly proportional to repo size
- Willingness to iterate on the review prompt for weeks

## Explicit non-goals for v1

- Tuned false-positive suppression: their bot has been beaten into silence by thousands of teams, yours has not
- Incremental reindexing and monorepo handling that does not choke on a 500k file tree
- Memory of past reviews so the same nit is not raised on every force push
- Team-level config, custom rule sets, and per-repo style learning from accepted or dismissed comments
- Bitbucket, GitLab, and self-hosted host support, plus SOC 2 paperwork your security team will ask for

## 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 self-hosted AI pull request reviewer for a single GitHub repository. No web UI, no accounts, no telemetry.

Stack, no substitutions: Python 3.12, FastAPI, uvicorn, SQLite for state, sqlite-vec for vector search, httpx for GitHub REST calls, OpenAI API for embeddings and review generation. Package with uv. Everything runs in one process plus one CLI.

Secrets in .env, loaded with python-dotenv: GITHUB_APP_ID, GITHUB_PRIVATE_KEY_PATH, GITHUB_WEBHOOK_SECRET, OPENAI_API_KEY, REPO_FULL_NAME.

Part 1, indexer CLI (index.py):
- Walk the local clone of the repo, respect .gitignore, skip binaries, lockfiles, and anything over 400 KB.
- Chunk source files by function or class using tree-sitter for Python, TypeScript, JavaScript, and Go; fall back to 60-line sliding windows with 10-line overlap for everything else.
- Embed each chunk, store text, path, start line, end line, git blob sha, and vector in SQLite.
- Support incremental reindex: given two git revisions, only re-embed chunks whose file blob sha changed. Print counts of added, updated, deleted chunks.

Part 2, webhook service (server.py):
- POST /webhook, verify the X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET, reject on mismatch.
- Handle pull_request opened and synchronize events only. Enqueue work in a SQLite-backed job table and return 202 immediately.
- A background worker fetches the PR diff, splits it per file hunk, and for each hunk retrieves the top 8 related chunks by vector similarity plus the full current version of the changed file if it is under 800 lines.
- Send one LLM call per changed file with the hunk, retrieved context, and a review prompt that demands: only comment on correctness, security, or clear API misuse; no style nits; no praise; no summaries; return an empty array when the change is fine.
- Model output must be strict JSON: a list of objects with path, line, severity, body. Validate with Pydantic and drop anything whose line is not inside the diff.

Part 3, dedupe and posting:
- Store a hash of path plus normalized comment body per PR. Never post the same finding twice across force pushes.
- Post surviving comments as a single GitHub review with inline comments, authenticated as the GitHub App via a short-lived installation token.
- Add a CLI command 'review-local PR_NUMBER' that prints what it would post without calling GitHub, for prompt tuning.

Out of scope: GitLab and Bitbucket, multi-repo support, a dashboard, learning from dismissed comments, autofix suggestions, chat replies to review threads.

Deliver a README with GitHub App setup steps, the exact permissions needed (contents read, pull requests write, metadata read), and how to expose the webhook locally with a tunnel. Include pytest coverage for signature verification, diff line mapping, and dedupe.

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

# Greptile indie build

## Goal

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

## Scope

A GitHub App that on each pull request retrieves semantically related code from a local embedding index of the repo and posts LLM-written inline review comments on the diff.

## 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:
- Tuned false-positive suppression: their bot has been beaten into silence by thousands of teams, yours has not
- Incremental reindexing and monorepo handling that does not choke on a 500k file tree
- Memory of past reviews so the same nit is not raised on every force push
- Team-level config, custom rule sets, and per-repo style learning from accepted or dismissed comments
- Bitbucket, GitLab, and self-hosted host support, plus SOC 2 paperwork your security team will ask for

If those capabilities are essential, use Greptile 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 a self-hosted AI pull request reviewer for a single GitHub repository. No web UI, no accounts, no telemetry.

Stack, no substitutions: Python 3.12, FastAPI, uvicorn, SQLite for state, sqlite-vec for vector search, httpx for GitHub REST calls, OpenAI API for embeddings and review generation. Package with uv. Everything runs in one process plus one CLI.

Secrets in .env, loaded with python-dotenv: GITHUB_APP_ID, GITHUB_PRIVATE_KEY_PATH, GITHUB_WEBHOOK_SECRET, OPENAI_API_KEY, REPO_FULL_NAME.

Part 1, indexer CLI (index.py):
- Walk the local clone of the repo, respect .gitignore, skip binaries, lockfiles, and anything over 400 KB.
- Chunk source files by function or class using tree-sitter for Python, TypeScript, JavaScript, and Go; fall back to 60-line sliding windows with 10-line overlap for everything else.
- Embed each chunk, store text, path, start line, end line, git blob sha, and vector in SQLite.
- Support incremental reindex: given two git revisions, only re-embed chunks whose file blob sha changed. Print counts of added, updated, deleted chunks.

Part 2, webhook service (server.py):
- POST /webhook, verify the X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET, reject on mismatch.
- Handle pull_request opened and synchronize events only. Enqueue work in a SQLite-backed job table and return 202 immediately.
- A background worker fetches the PR diff, splits it per file hunk, and for each hunk retrieves the top 8 related chunks by vector similarity plus the full current version of the changed file if it is under 800 lines.
- Send one LLM call per changed file with the hunk, retrieved context, and a review prompt that demands: only comment on correctness, security, or clear API misuse; no style nits; no praise; no summaries; return an empty array when the change is fine.
- Model output must be strict JSON: a list of objects with path, line, severity, body. Validate with Pydantic and drop anything whose line is not inside the diff.

Part 3, dedupe and posting:
- Store a hash of path plus normalized comment body per PR. Never post the same finding twice across force pushes.
- Post surviving comments as a single GitHub review with inline comments, authenticated as the GitHub App via a short-lived installation token.
- Add a CLI command 'review-local PR_NUMBER' that prints what it would post without calling GitHub, for prompt tuning.

Out of scope: GitLab and Bitbucket, multi-repo support, a dashboard, learning from dismissed comments, autofix suggestions, chat replies to review threads.

Deliver a README with GitHub App setup steps, the exact permissions needed (contents read, pull requests write, metadata read), and how to expose the webhook locally with a tunnel. Include pytest coverage for signature verification, diff line mapping, and dedupe.

## Required capabilities

- GitHub App registration with webhook secret and private key
- An LLM API key (embeddings plus a strong reasoning model)
- A small always-on host or tunnel to receive webhooks
- Local disk for the vector index, roughly proportional to repo size
- Willingness to iterate on the review prompt for weeks

## 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.

# Greptile product brief

## Problem

The mechanical core is genuinely a weekend project: a GitHub App that catches pull request webhooks, pulls the diff, retrieves related code from an embedding index of the repo, and posts inline comments from an LLM. You can get to first useful comment in an afternoon. What you will not get in one sitting is signal quality, which is the entire product: knowing when to shut up, not re-flagging the same nit on every push, understanding a monorepo without blowing the context window, and keeping the index fresh without a full reindex on every merge. Expect a bot that is impressive on day one and muted by the team on day nine. Worth building if you own the repo and enjoy tuning prompts; not worth building to save a per-seat fee across a real engineering org.

## Product outcome

A GitHub App that on each pull request retrieves semantically related code from a local embedding index of the repo and posts LLM-written inline review comments on the diff.

## Target user

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

## Required capabilities

- GitHub App registration with webhook secret and private key
- An LLM API key (embeddings plus a strong reasoning model)
- A small always-on host or tunnel to receive webhooks
- Local disk for the vector index, roughly proportional to repo size
- Willingness to iterate on the review prompt for weeks

## Explicit non-goals for v1

- Tuned false-positive suppression: their bot has been beaten into silence by thousands of teams, yours has not
- Incremental reindexing and monorepo handling that does not choke on a 500k file tree
- Memory of past reviews so the same nit is not raised on every force push
- Team-level config, custom rule sets, and per-repo style learning from accepted or dismissed comments
- Bitbucket, GitLab, and self-hosted host support, plus SOC 2 paperwork your security team will ask for

## 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 a self-hosted AI pull request reviewer for a single GitHub repository. No web UI, no accounts, no telemetry.

Stack, no substitutions: Python 3.12, FastAPI, uvicorn, SQLite for state, sqlite-vec for vector search, httpx for GitHub REST calls, OpenAI API for embeddings and review generation. Package with uv. Everything runs in one process plus one CLI.

Secrets in .env, loaded with python-dotenv: GITHUB_APP_ID, GITHUB_PRIVATE_KEY_PATH, GITHUB_WEBHOOK_SECRET, OPENAI_API_KEY, REPO_FULL_NAME.

Part 1, indexer CLI (index.py):
- Walk the local clone of the repo, respect .gitignore, skip binaries, lockfiles, and anything over 400 KB.
- Chunk source files by function or class using tree-sitter for Python, TypeScript, JavaScript, and Go; fall back to 60-line sliding windows with 10-line overlap for everything else.
- Embed each chunk, store text, path, start line, end line, git blob sha, and vector in SQLite.
- Support incremental reindex: given two git revisions, only re-embed chunks whose file blob sha changed. Print counts of added, updated, deleted chunks.

Part 2, webhook service (server.py):
- POST /webhook, verify the X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET, reject on mismatch.
- Handle pull_request opened and synchronize events only. Enqueue work in a SQLite-backed job table and return 202 immediately.
- A background worker fetches the PR diff, splits it per file hunk, and for each hunk retrieves the top 8 related chunks by vector similarity plus the full current version of the changed file if it is under 800 lines.
- Send one LLM call per changed file with the hunk, retrieved context, and a review prompt that demands: only comment on correctness, security, or clear API misuse; no style nits; no praise; no summaries; return an empty array when the change is fine.
- Model output must be strict JSON: a list of objects with path, line, severity, body. Validate with Pydantic and drop anything whose line is not inside the diff.

Part 3, dedupe and posting:
- Store a hash of path plus normalized comment body per PR. Never post the same finding twice across force pushes.
- Post surviving comments as a single GitHub review with inline comments, authenticated as the GitHub App via a short-lived installation token.
- Add a CLI command 'review-local PR_NUMBER' that prints what it would post without calling GitHub, for prompt tuning.

Out of scope: GitLab and Bitbucket, multi-repo support, a dashboard, learning from dismissed comments, autofix suggestions, chat replies to review threads.

Deliver a README with GitHub App setup steps, the exact permissions needed (contents read, pull requests write, metadata read), and how to expose the webhook locally with a tunnel. Include pytest coverage for signature verification, diff line mapping, and dedupe.

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

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