Vibecode SubSignal

You are building a lean indie version of SubSignal.

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 =====
# SubSignal indie build

## Goal

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

## Scope

Poll a list of subreddits on a schedule, filter new posts by keyword, ask one model whether each is a buying signal for your product, and send the hits to Slack or email.

## 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:
- a historical archive you cannot backfill from the live API
- ranking a lead against everything seen before, instead of judging it alone
- a curated subreddit and keyword set you did not have to discover
- deduplication and history across runs
- someone tracking Reddit API terms and rate-limit changes for you

If those capabilities are essential, use PRAW 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 Reddit buying-signal monitor inspired by SubSignal.
Use exactly this stack: Node.js 22 + TypeScript + SQLite.
Primary job: on a schedule, fetch new posts and comments from a configured list of subreddits, ask one language model whether each one describes a problem the user's product solves, and deliver the matches to Slack or email with a link and a one-line reason.
Start from an empty folder and create the complete working project.
Use the official Reddit API with credentials from .env and respect its rate limits and terms of use; never scrape around them.
Store every item you have seen in SQLite so the same thread is never delivered twice, including across restarts.
Let the user describe their product in plain language in a config file, and build the classification prompt from that rather than from a hardcoded keyword list.
Score each match and let the user set a threshold, so the digest can be tightened without code changes.
Send a digest rather than one alert per hit, and make the schedule configurable.
Alert the user when a run fails or returns nothing for several consecutive runs - a silent crawler is the main failure mode of this kind of tool.
Put every secret in .env and provide .env.example.
Deliberately exclude these paid-product advantages: a curated subreddit and keyword set, accumulated cross-customer history, managed uptime.
Do not fake integrations, deliverability or data you do not have.
Write unit tests for deduplication and scoring, and one end-to-end smoke test against recorded fixtures rather than the live API.
Create a README with setup, Reddit API terms, cost estimate, architecture and limitations.
Run the tests and build before finishing, then fix what fails.

## Required capabilities

- Reddit API credentials, within Reddit's terms of use
- OpenAI or Anthropic API key in .env
- Node.js 22 and a scheduler such as cron
- SQLite to remember what you have already seen

## 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 SubSignal.

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 =====
# SubSignal indie build

## Goal

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

## Scope

Poll a list of subreddits on a schedule, filter new posts by keyword, ask one model whether each is a buying signal for your product, and send the hits to Slack or email.

## 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:
- a historical archive you cannot backfill from the live API
- ranking a lead against everything seen before, instead of judging it alone
- a curated subreddit and keyword set you did not have to discover
- deduplication and history across runs
- someone tracking Reddit API terms and rate-limit changes for you

If those capabilities are essential, use PRAW 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 Reddit buying-signal monitor inspired by SubSignal.
Use exactly this stack: Node.js 22 + TypeScript + SQLite.
Primary job: on a schedule, fetch new posts and comments from a configured list of subreddits, ask one language model whether each one describes a problem the user's product solves, and deliver the matches to Slack or email with a link and a one-line reason.
Start from an empty folder and create the complete working project.
Use the official Reddit API with credentials from .env and respect its rate limits and terms of use; never scrape around them.
Store every item you have seen in SQLite so the same thread is never delivered twice, including across restarts.
Let the user describe their product in plain language in a config file, and build the classification prompt from that rather than from a hardcoded keyword list.
Score each match and let the user set a threshold, so the digest can be tightened without code changes.
Send a digest rather than one alert per hit, and make the schedule configurable.
Alert the user when a run fails or returns nothing for several consecutive runs - a silent crawler is the main failure mode of this kind of tool.
Put every secret in .env and provide .env.example.
Deliberately exclude these paid-product advantages: a curated subreddit and keyword set, accumulated cross-customer history, managed uptime.
Do not fake integrations, deliverability or data you do not have.
Write unit tests for deduplication and scoring, and one end-to-end smoke test against recorded fixtures rather than the live API.
Create a README with setup, Reddit API terms, cost estimate, architecture and limitations.
Run the tests and build before finishing, then fix what fails.

## Required capabilities

- Reddit API credentials, within Reddit's terms of use
- OpenAI or Anthropic API key in .env
- Node.js 22 and a scheduler such as cron
- SQLite to remember what you have already seen

## 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 SubSignal.

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 =====
# SubSignal product brief

## Problem

Do not mistake the loop for the product. Polling subreddits and asking a model "is this a buying signal?" is an evening, and it is also the part that does not matter. What makes this useful is the accumulated archive behind it: years of threads at a scale you cannot backfill, which is what lets it tell a recurring complaint from a one-off and rank a lead against everything already seen. Your build starts empty on day one and stays empty, because Reddit will not sell you the past. That is a data problem, not a code problem.

## Product outcome

Poll a list of subreddits on a schedule, filter new posts by keyword, ask one model whether each is a buying signal for your product, and send the hits to Slack or email.

## Target user

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

## Required capabilities

- Reddit API credentials, within Reddit's terms of use
- OpenAI or Anthropic API key in .env
- Node.js 22 and a scheduler such as cron
- SQLite to remember what you have already seen

## Explicit non-goals for v1

- a historical archive you cannot backfill from the live API
- ranking a lead against everything seen before, instead of judging it alone
- a curated subreddit and keyword set you did not have to discover
- deduplication and history across runs
- someone tracking Reddit API terms and rate-limit changes for you

## 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 Reddit buying-signal monitor inspired by SubSignal.
Use exactly this stack: Node.js 22 + TypeScript + SQLite.
Primary job: on a schedule, fetch new posts and comments from a configured list of subreddits, ask one language model whether each one describes a problem the user's product solves, and deliver the matches to Slack or email with a link and a one-line reason.
Start from an empty folder and create the complete working project.
Use the official Reddit API with credentials from .env and respect its rate limits and terms of use; never scrape around them.
Store every item you have seen in SQLite so the same thread is never delivered twice, including across restarts.
Let the user describe their product in plain language in a config file, and build the classification prompt from that rather than from a hardcoded keyword list.
Score each match and let the user set a threshold, so the digest can be tightened without code changes.
Send a digest rather than one alert per hit, and make the schedule configurable.
Alert the user when a run fails or returns nothing for several consecutive runs - a silent crawler is the main failure mode of this kind of tool.
Put every secret in .env and provide .env.example.
Deliberately exclude these paid-product advantages: a curated subreddit and keyword set, accumulated cross-customer history, managed uptime.
Do not fake integrations, deliverability or data you do not have.
Write unit tests for deduplication and scoring, and one end-to-end smoke test against recorded fixtures rather than the live API.
Create a README with setup, Reddit API terms, cost estimate, architecture and limitations.
Run the tests and build before finishing, then fix what fails.

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

# SubSignal indie build

## Goal

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

## Scope

Poll a list of subreddits on a schedule, filter new posts by keyword, ask one model whether each is a buying signal for your product, and send the hits to Slack or email.

## 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:
- a historical archive you cannot backfill from the live API
- ranking a lead against everything seen before, instead of judging it alone
- a curated subreddit and keyword set you did not have to discover
- deduplication and history across runs
- someone tracking Reddit API terms and rate-limit changes for you

If those capabilities are essential, use PRAW 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 Reddit buying-signal monitor inspired by SubSignal.
Use exactly this stack: Node.js 22 + TypeScript + SQLite.
Primary job: on a schedule, fetch new posts and comments from a configured list of subreddits, ask one language model whether each one describes a problem the user's product solves, and deliver the matches to Slack or email with a link and a one-line reason.
Start from an empty folder and create the complete working project.
Use the official Reddit API with credentials from .env and respect its rate limits and terms of use; never scrape around them.
Store every item you have seen in SQLite so the same thread is never delivered twice, including across restarts.
Let the user describe their product in plain language in a config file, and build the classification prompt from that rather than from a hardcoded keyword list.
Score each match and let the user set a threshold, so the digest can be tightened without code changes.
Send a digest rather than one alert per hit, and make the schedule configurable.
Alert the user when a run fails or returns nothing for several consecutive runs - a silent crawler is the main failure mode of this kind of tool.
Put every secret in .env and provide .env.example.
Deliberately exclude these paid-product advantages: a curated subreddit and keyword set, accumulated cross-customer history, managed uptime.
Do not fake integrations, deliverability or data you do not have.
Write unit tests for deduplication and scoring, and one end-to-end smoke test against recorded fixtures rather than the live API.
Create a README with setup, Reddit API terms, cost estimate, architecture and limitations.
Run the tests and build before finishing, then fix what fails.

## Required capabilities

- Reddit API credentials, within Reddit's terms of use
- OpenAI or Anthropic API key in .env
- Node.js 22 and a scheduler such as cron
- SQLite to remember what you have already seen

## 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.

# SubSignal product brief

## Problem

Do not mistake the loop for the product. Polling subreddits and asking a model "is this a buying signal?" is an evening, and it is also the part that does not matter. What makes this useful is the accumulated archive behind it: years of threads at a scale you cannot backfill, which is what lets it tell a recurring complaint from a one-off and rank a lead against everything already seen. Your build starts empty on day one and stays empty, because Reddit will not sell you the past. That is a data problem, not a code problem.

## Product outcome

Poll a list of subreddits on a schedule, filter new posts by keyword, ask one model whether each is a buying signal for your product, and send the hits to Slack or email.

## Target user

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

## Required capabilities

- Reddit API credentials, within Reddit's terms of use
- OpenAI or Anthropic API key in .env
- Node.js 22 and a scheduler such as cron
- SQLite to remember what you have already seen

## Explicit non-goals for v1

- a historical archive you cannot backfill from the live API
- ranking a lead against everything seen before, instead of judging it alone
- a curated subreddit and keyword set you did not have to discover
- deduplication and history across runs
- someone tracking Reddit API terms and rate-limit changes for you

## 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 Reddit buying-signal monitor inspired by SubSignal.
Use exactly this stack: Node.js 22 + TypeScript + SQLite.
Primary job: on a schedule, fetch new posts and comments from a configured list of subreddits, ask one language model whether each one describes a problem the user's product solves, and deliver the matches to Slack or email with a link and a one-line reason.
Start from an empty folder and create the complete working project.
Use the official Reddit API with credentials from .env and respect its rate limits and terms of use; never scrape around them.
Store every item you have seen in SQLite so the same thread is never delivered twice, including across restarts.
Let the user describe their product in plain language in a config file, and build the classification prompt from that rather than from a hardcoded keyword list.
Score each match and let the user set a threshold, so the digest can be tightened without code changes.
Send a digest rather than one alert per hit, and make the schedule configurable.
Alert the user when a run fails or returns nothing for several consecutive runs - a silent crawler is the main failure mode of this kind of tool.
Put every secret in .env and provide .env.example.
Deliberately exclude these paid-product advantages: a curated subreddit and keyword set, accumulated cross-customer history, managed uptime.
Do not fake integrations, deliverability or data you do not have.
Write unit tests for deduplication and scoring, and one end-to-end smoke test against recorded fixtures rather than the live API.
Create a README with setup, Reddit API terms, cost estimate, architecture and limitations.
Run the tests and build before finishing, then fix what fails.

## 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 SubSignal 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