Vibecode SnitchFeed

You are building a lean indie version of SnitchFeed.

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 =====
# SnitchFeed indie build

## Goal

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

## Scope

Poll a couple of free, open feeds (Reddit's API, Bluesky's firehose, Hacker News's Algolia API) for keyword matches, run each hit through an LLM for a rough relevance/sentiment tag, and push matches to a Slack or Discord webhook.

## 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:
- X/Twitter and LinkedIn coverage entirely (neither has a free public search API), so two of the five platforms are simply gone
- one aggregated, deduplicated stream across five platforms instead of five half-working pollers
- the noise-reduction stack: boolean query grammar, AI fit scores, sentiment, intent tags, and automated noise auditing that keeps tuning what gets through
- a real-time dashboard with curated feeds, saved views, and analytics reports instead of a Slack ping you learn to ignore
- an agent-native surface: an MCP server and public REST API so your own AI agents can search mentions, create listeners, and act on intent directly

If those capabilities are essential, use Huginn 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 personal social-listening tool inspired by SnitchFeed, starting from an empty folder. This is an honest consolation build: it watches free, open feeds only and does not replace SnitchFeed's aggregation, tuned scoring, or automation layer.

Stack (use exactly this): Next.js 15 + TypeScript + PostgreSQL + BullMQ + Redis.

Core loop:
- Let me define keywords/brand terms and a poll interval per source.
- Poll Reddit's API, Bluesky's search, and Hacker News's Algolia API via a BullMQ worker on a cron schedule; dedupe matches by source + id.
- Score each match's relevance and sentiment with one LLM call (OpenAI or Anthropic); store both alongside the raw post.
- Push new matches to a Slack or Discord incoming webhook.
- Smallest polished UI that closes the loop: add a keyword, watch matches stream in, mark them read or irrelevant.

Rules:
- Single-user and private by default; all data in local Postgres.
- Every API key, app password, and webhook URL in .env, with .env.example provided; never log secrets; validate untrusted input.
- No analytics, telemetry, ads, or accounts beyond what's declared.
- Clear empty, loading, validation, success, and failure states.

Deliberately out of scope (do not fake these): X/Twitter and LinkedIn coverage, cross-platform aggregation and dedup at scale, continuously tuned relevance scoring and noise auditing, a live real-time dashboard, and any agent-facing API/MCP layer.

Finish line:
- Unit tests for the dedupe logic and the scoring call, plus one end-to-end smoke test: keyword added, fake match flows through to a webhook call.
- README covering setup, the exact free APIs used and their rate limits, and this build's limitations versus a paid multi-source listening tool.
- Scripts for install, development, test, build, and a production-style local run.
- Run the tests and the build before finishing; fix errors rather than describing them.

## Required capabilities

- Reddit API app credentials (free)
- Bluesky app password for firehose/search access
- Hacker News Algolia API (no key required)
- OpenAI/Anthropic API key for relevance & sentiment tagging
- hosted Postgres + a cron worker or queue
- Slack/Discord incoming webhook URL(s)

## 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 SnitchFeed.

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 =====
# SnitchFeed indie build

## Goal

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

## Scope

Poll a couple of free, open feeds (Reddit's API, Bluesky's firehose, Hacker News's Algolia API) for keyword matches, run each hit through an LLM for a rough relevance/sentiment tag, and push matches to a Slack or Discord webhook.

## 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:
- X/Twitter and LinkedIn coverage entirely (neither has a free public search API), so two of the five platforms are simply gone
- one aggregated, deduplicated stream across five platforms instead of five half-working pollers
- the noise-reduction stack: boolean query grammar, AI fit scores, sentiment, intent tags, and automated noise auditing that keeps tuning what gets through
- a real-time dashboard with curated feeds, saved views, and analytics reports instead of a Slack ping you learn to ignore
- an agent-native surface: an MCP server and public REST API so your own AI agents can search mentions, create listeners, and act on intent directly

If those capabilities are essential, use Huginn 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 personal social-listening tool inspired by SnitchFeed, starting from an empty folder. This is an honest consolation build: it watches free, open feeds only and does not replace SnitchFeed's aggregation, tuned scoring, or automation layer.

Stack (use exactly this): Next.js 15 + TypeScript + PostgreSQL + BullMQ + Redis.

Core loop:
- Let me define keywords/brand terms and a poll interval per source.
- Poll Reddit's API, Bluesky's search, and Hacker News's Algolia API via a BullMQ worker on a cron schedule; dedupe matches by source + id.
- Score each match's relevance and sentiment with one LLM call (OpenAI or Anthropic); store both alongside the raw post.
- Push new matches to a Slack or Discord incoming webhook.
- Smallest polished UI that closes the loop: add a keyword, watch matches stream in, mark them read or irrelevant.

Rules:
- Single-user and private by default; all data in local Postgres.
- Every API key, app password, and webhook URL in .env, with .env.example provided; never log secrets; validate untrusted input.
- No analytics, telemetry, ads, or accounts beyond what's declared.
- Clear empty, loading, validation, success, and failure states.

Deliberately out of scope (do not fake these): X/Twitter and LinkedIn coverage, cross-platform aggregation and dedup at scale, continuously tuned relevance scoring and noise auditing, a live real-time dashboard, and any agent-facing API/MCP layer.

Finish line:
- Unit tests for the dedupe logic and the scoring call, plus one end-to-end smoke test: keyword added, fake match flows through to a webhook call.
- README covering setup, the exact free APIs used and their rate limits, and this build's limitations versus a paid multi-source listening tool.
- Scripts for install, development, test, build, and a production-style local run.
- Run the tests and the build before finishing; fix errors rather than describing them.

## Required capabilities

- Reddit API app credentials (free)
- Bluesky app password for firehose/search access
- Hacker News Algolia API (no key required)
- OpenAI/Anthropic API key for relevance & sentiment tagging
- hosted Postgres + a cron worker or queue
- Slack/Discord incoming webhook URL(s)

## 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 SnitchFeed.

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 =====
# SnitchFeed product brief

## Problem

A weekend build gets you a keyword matcher; SnitchFeed is a signal refinery. Five platforms in one stream (Reddit, X, LinkedIn, Bluesky, Hacker News), run through layered filtering: boolean queries, AI scoring for relevance, sentiment, and buying intent, and noise auditing that keeps trimming. What comes out is a shortlist of threads worth answering, delivered where you act: Slack, Discord, a live dashboard, or your own AI agent via MCP and REST API. To be fair: if you only care about Reddit and Hacker News, a DIY build gets you further than the verdict suggests. It's the X and LinkedIn coverage, the cross-platform aggregation, and the tuned scoring that a one-shot build can't reach.

## Product outcome

Poll a couple of free, open feeds (Reddit's API, Bluesky's firehose, Hacker News's Algolia API) for keyword matches, run each hit through an LLM for a rough relevance/sentiment tag, and push matches to a Slack or Discord webhook.

## Target user

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

## Required capabilities

- Reddit API app credentials (free)
- Bluesky app password for firehose/search access
- Hacker News Algolia API (no key required)
- OpenAI/Anthropic API key for relevance & sentiment tagging
- hosted Postgres + a cron worker or queue
- Slack/Discord incoming webhook URL(s)

## Explicit non-goals for v1

- X/Twitter and LinkedIn coverage entirely (neither has a free public search API), so two of the five platforms are simply gone
- one aggregated, deduplicated stream across five platforms instead of five half-working pollers
- the noise-reduction stack: boolean query grammar, AI fit scores, sentiment, intent tags, and automated noise auditing that keeps tuning what gets through
- a real-time dashboard with curated feeds, saved views, and analytics reports instead of a Slack ping you learn to ignore
- an agent-native surface: an MCP server and public REST API so your own AI agents can search mentions, create listeners, and act on intent directly

## 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 personal social-listening tool inspired by SnitchFeed, starting from an empty folder. This is an honest consolation build: it watches free, open feeds only and does not replace SnitchFeed's aggregation, tuned scoring, or automation layer.

Stack (use exactly this): Next.js 15 + TypeScript + PostgreSQL + BullMQ + Redis.

Core loop:
- Let me define keywords/brand terms and a poll interval per source.
- Poll Reddit's API, Bluesky's search, and Hacker News's Algolia API via a BullMQ worker on a cron schedule; dedupe matches by source + id.
- Score each match's relevance and sentiment with one LLM call (OpenAI or Anthropic); store both alongside the raw post.
- Push new matches to a Slack or Discord incoming webhook.
- Smallest polished UI that closes the loop: add a keyword, watch matches stream in, mark them read or irrelevant.

Rules:
- Single-user and private by default; all data in local Postgres.
- Every API key, app password, and webhook URL in .env, with .env.example provided; never log secrets; validate untrusted input.
- No analytics, telemetry, ads, or accounts beyond what's declared.
- Clear empty, loading, validation, success, and failure states.

Deliberately out of scope (do not fake these): X/Twitter and LinkedIn coverage, cross-platform aggregation and dedup at scale, continuously tuned relevance scoring and noise auditing, a live real-time dashboard, and any agent-facing API/MCP layer.

Finish line:
- Unit tests for the dedupe logic and the scoring call, plus one end-to-end smoke test: keyword added, fake match flows through to a webhook call.
- README covering setup, the exact free APIs used and their rate limits, and this build's limitations versus a paid multi-source listening tool.
- Scripts for install, development, test, build, and a production-style local run.
- Run the tests and the build before finishing; fix errors rather than describing them.

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

# SnitchFeed indie build

## Goal

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

## Scope

Poll a couple of free, open feeds (Reddit's API, Bluesky's firehose, Hacker News's Algolia API) for keyword matches, run each hit through an LLM for a rough relevance/sentiment tag, and push matches to a Slack or Discord webhook.

## 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:
- X/Twitter and LinkedIn coverage entirely (neither has a free public search API), so two of the five platforms are simply gone
- one aggregated, deduplicated stream across five platforms instead of five half-working pollers
- the noise-reduction stack: boolean query grammar, AI fit scores, sentiment, intent tags, and automated noise auditing that keeps tuning what gets through
- a real-time dashboard with curated feeds, saved views, and analytics reports instead of a Slack ping you learn to ignore
- an agent-native surface: an MCP server and public REST API so your own AI agents can search mentions, create listeners, and act on intent directly

If those capabilities are essential, use Huginn 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 personal social-listening tool inspired by SnitchFeed, starting from an empty folder. This is an honest consolation build: it watches free, open feeds only and does not replace SnitchFeed's aggregation, tuned scoring, or automation layer.

Stack (use exactly this): Next.js 15 + TypeScript + PostgreSQL + BullMQ + Redis.

Core loop:
- Let me define keywords/brand terms and a poll interval per source.
- Poll Reddit's API, Bluesky's search, and Hacker News's Algolia API via a BullMQ worker on a cron schedule; dedupe matches by source + id.
- Score each match's relevance and sentiment with one LLM call (OpenAI or Anthropic); store both alongside the raw post.
- Push new matches to a Slack or Discord incoming webhook.
- Smallest polished UI that closes the loop: add a keyword, watch matches stream in, mark them read or irrelevant.

Rules:
- Single-user and private by default; all data in local Postgres.
- Every API key, app password, and webhook URL in .env, with .env.example provided; never log secrets; validate untrusted input.
- No analytics, telemetry, ads, or accounts beyond what's declared.
- Clear empty, loading, validation, success, and failure states.

Deliberately out of scope (do not fake these): X/Twitter and LinkedIn coverage, cross-platform aggregation and dedup at scale, continuously tuned relevance scoring and noise auditing, a live real-time dashboard, and any agent-facing API/MCP layer.

Finish line:
- Unit tests for the dedupe logic and the scoring call, plus one end-to-end smoke test: keyword added, fake match flows through to a webhook call.
- README covering setup, the exact free APIs used and their rate limits, and this build's limitations versus a paid multi-source listening tool.
- Scripts for install, development, test, build, and a production-style local run.
- Run the tests and the build before finishing; fix errors rather than describing them.

## Required capabilities

- Reddit API app credentials (free)
- Bluesky app password for firehose/search access
- Hacker News Algolia API (no key required)
- OpenAI/Anthropic API key for relevance & sentiment tagging
- hosted Postgres + a cron worker or queue
- Slack/Discord incoming webhook URL(s)

## 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.

# SnitchFeed product brief

## Problem

A weekend build gets you a keyword matcher; SnitchFeed is a signal refinery. Five platforms in one stream (Reddit, X, LinkedIn, Bluesky, Hacker News), run through layered filtering: boolean queries, AI scoring for relevance, sentiment, and buying intent, and noise auditing that keeps trimming. What comes out is a shortlist of threads worth answering, delivered where you act: Slack, Discord, a live dashboard, or your own AI agent via MCP and REST API. To be fair: if you only care about Reddit and Hacker News, a DIY build gets you further than the verdict suggests. It's the X and LinkedIn coverage, the cross-platform aggregation, and the tuned scoring that a one-shot build can't reach.

## Product outcome

Poll a couple of free, open feeds (Reddit's API, Bluesky's firehose, Hacker News's Algolia API) for keyword matches, run each hit through an LLM for a rough relevance/sentiment tag, and push matches to a Slack or Discord webhook.

## Target user

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

## Required capabilities

- Reddit API app credentials (free)
- Bluesky app password for firehose/search access
- Hacker News Algolia API (no key required)
- OpenAI/Anthropic API key for relevance & sentiment tagging
- hosted Postgres + a cron worker or queue
- Slack/Discord incoming webhook URL(s)

## Explicit non-goals for v1

- X/Twitter and LinkedIn coverage entirely (neither has a free public search API), so two of the five platforms are simply gone
- one aggregated, deduplicated stream across five platforms instead of five half-working pollers
- the noise-reduction stack: boolean query grammar, AI fit scores, sentiment, intent tags, and automated noise auditing that keeps tuning what gets through
- a real-time dashboard with curated feeds, saved views, and analytics reports instead of a Slack ping you learn to ignore
- an agent-native surface: an MCP server and public REST API so your own AI agents can search mentions, create listeners, and act on intent directly

## 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 personal social-listening tool inspired by SnitchFeed, starting from an empty folder. This is an honest consolation build: it watches free, open feeds only and does not replace SnitchFeed's aggregation, tuned scoring, or automation layer.

Stack (use exactly this): Next.js 15 + TypeScript + PostgreSQL + BullMQ + Redis.

Core loop:
- Let me define keywords/brand terms and a poll interval per source.
- Poll Reddit's API, Bluesky's search, and Hacker News's Algolia API via a BullMQ worker on a cron schedule; dedupe matches by source + id.
- Score each match's relevance and sentiment with one LLM call (OpenAI or Anthropic); store both alongside the raw post.
- Push new matches to a Slack or Discord incoming webhook.
- Smallest polished UI that closes the loop: add a keyword, watch matches stream in, mark them read or irrelevant.

Rules:
- Single-user and private by default; all data in local Postgres.
- Every API key, app password, and webhook URL in .env, with .env.example provided; never log secrets; validate untrusted input.
- No analytics, telemetry, ads, or accounts beyond what's declared.
- Clear empty, loading, validation, success, and failure states.

Deliberately out of scope (do not fake these): X/Twitter and LinkedIn coverage, cross-platform aggregation and dedup at scale, continuously tuned relevance scoring and noise auditing, a live real-time dashboard, and any agent-facing API/MCP layer.

Finish line:
- Unit tests for the dedupe logic and the scoring call, plus one end-to-end smoke test: keyword added, fake match flows through to a webhook call.
- README covering setup, the exact free APIs used and their rate limits, and this build's limitations versus a paid multi-source listening tool.
- Scripts for install, development, test, build, and a production-style local run.
- Run the tests and the build before finishing; fix errors rather than describing them.

## 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 SnitchFeed 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