Vibecode FlowHunt

You are building a lean indie version of FlowHunt.

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 =====
# FlowHunt indie build

## Goal

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

## Scope

Wire one AI agent to one or two data sources, route a prompt through a chosen model, run a fixed tool/action, and return or store the result on a schedule or 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:
- 100+ maintained connectors and the OAuth apps, token refresh, and schema-drift upkeep behind them
- the no-code visual builder that lets non-developers assemble and edit agents
- one-provider model routing with credit metering across OpenAI, Anthropic, Google, Meta, and Mistral
- managed hosting, execution capacity, and reliability instead of a box you babysit
- team workspaces, shared agents, and role permissions

If those capabilities are essential, use n8n 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 replacement for one FlowHunt agent in an empty repository.
Use TypeScript, Node.js 22, SQLite via better-sqlite3, and the Vercel AI SDK; do not offer alternative stacks.
Scope: a single configurable AI agent, not a general no-code platform. Pick one job and do it well.
The core loop is: load a knowledge base, accept an input (webhook or CLI), route it through a chosen model with a system prompt and a fixed set of tools, take one action, and store the result.
agent.json defines the system prompt, the model (OpenAI or Anthropic, selectable), the tools it may call, and the schedule or trigger.
Ingest a local docs/ folder into a SQLite vector table for retrieval; re-index on a command.
Implement exactly two real tools, an HTTP fetch/POST to a named API using credentials from .env, and a write-to-file or write-to-db action; document how to add a third.
Run on a webhook endpoint and on a cron schedule; keep an immutable log of every run: input, retrieved chunks, model, tokens, tool calls, output, and errors.
Add retries with backoff on 429 and 5xx, a per-run token budget cap, and visible failed runs rather than silent drops.
Put all secrets in .env, ship .env.example, and never commit credentials.
Store all data locally by default and make CSV export straightforward.
Include clear empty, loading, success, and recoverable error states in a plain server-rendered run dashboard.
Write focused tests for retrieval, one tool call, and one end-to-end happy path.
Create a README with setup, the agent config schema, how to add a tool, data location, and cost notes.
Do not add accounts, billing, telemetry, a visual builder, or a connector marketplace.
Deliberately leave out multi-tenant workspaces and team permissions.
Deliberately leave out one-click OAuth for dozens of third-party apps.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- OpenAI or Anthropic API key (and any other model providers you route to)
- API or OAuth credentials for each tool the agent actually touches
- a vector store or SQLite for the knowledge base
- a scheduler or webhook endpoint
- a place to host the runner

## 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 FlowHunt.

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 =====
# FlowHunt indie build

## Goal

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

## Scope

Wire one AI agent to one or two data sources, route a prompt through a chosen model, run a fixed tool/action, and return or store the result on a schedule or 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:
- 100+ maintained connectors and the OAuth apps, token refresh, and schema-drift upkeep behind them
- the no-code visual builder that lets non-developers assemble and edit agents
- one-provider model routing with credit metering across OpenAI, Anthropic, Google, Meta, and Mistral
- managed hosting, execution capacity, and reliability instead of a box you babysit
- team workspaces, shared agents, and role permissions

If those capabilities are essential, use n8n 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 replacement for one FlowHunt agent in an empty repository.
Use TypeScript, Node.js 22, SQLite via better-sqlite3, and the Vercel AI SDK; do not offer alternative stacks.
Scope: a single configurable AI agent, not a general no-code platform. Pick one job and do it well.
The core loop is: load a knowledge base, accept an input (webhook or CLI), route it through a chosen model with a system prompt and a fixed set of tools, take one action, and store the result.
agent.json defines the system prompt, the model (OpenAI or Anthropic, selectable), the tools it may call, and the schedule or trigger.
Ingest a local docs/ folder into a SQLite vector table for retrieval; re-index on a command.
Implement exactly two real tools, an HTTP fetch/POST to a named API using credentials from .env, and a write-to-file or write-to-db action; document how to add a third.
Run on a webhook endpoint and on a cron schedule; keep an immutable log of every run: input, retrieved chunks, model, tokens, tool calls, output, and errors.
Add retries with backoff on 429 and 5xx, a per-run token budget cap, and visible failed runs rather than silent drops.
Put all secrets in .env, ship .env.example, and never commit credentials.
Store all data locally by default and make CSV export straightforward.
Include clear empty, loading, success, and recoverable error states in a plain server-rendered run dashboard.
Write focused tests for retrieval, one tool call, and one end-to-end happy path.
Create a README with setup, the agent config schema, how to add a tool, data location, and cost notes.
Do not add accounts, billing, telemetry, a visual builder, or a connector marketplace.
Deliberately leave out multi-tenant workspaces and team permissions.
Deliberately leave out one-click OAuth for dozens of third-party apps.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- OpenAI or Anthropic API key (and any other model providers you route to)
- API or OAuth credentials for each tool the agent actually touches
- a vector store or SQLite for the knowledge base
- a scheduler or webhook endpoint
- a place to host the runner

## 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 FlowHunt.

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 =====
# FlowHunt product brief

## Problem

You can build one agent; you cannot build FlowHunt. A single-purpose AI agent, take a knowledge base or a data source, route a prompt through a model, take an action, return the result, is a one-sitting build. But the paid product's value sits outside that solo rebuild: a hosted runner that stays up, 100+ maintained connectors with all the OAuth and schema-drift upkeep behind them, one-provider model routing with credit accounting, a no-code visual builder aimed at people who will not write code, and team workspaces. That is integration breadth plus managed infrastructure, which is a structural moat, not polish. The prompt below is the honest consolation build: the one workflow you actually run, self-hosted, and you own its upkeep from then on.

## Product outcome

Wire one AI agent to one or two data sources, route a prompt through a chosen model, run a fixed tool/action, and return or store the result on a schedule or webhook.

## Target user

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

## Required capabilities

- OpenAI or Anthropic API key (and any other model providers you route to)
- API or OAuth credentials for each tool the agent actually touches
- a vector store or SQLite for the knowledge base
- a scheduler or webhook endpoint
- a place to host the runner

## Explicit non-goals for v1

- 100+ maintained connectors and the OAuth apps, token refresh, and schema-drift upkeep behind them
- the no-code visual builder that lets non-developers assemble and edit agents
- one-provider model routing with credit metering across OpenAI, Anthropic, Google, Meta, and Mistral
- managed hosting, execution capacity, and reliability instead of a box you babysit
- team workspaces, shared agents, and role permissions

## 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 replacement for one FlowHunt agent in an empty repository.
Use TypeScript, Node.js 22, SQLite via better-sqlite3, and the Vercel AI SDK; do not offer alternative stacks.
Scope: a single configurable AI agent, not a general no-code platform. Pick one job and do it well.
The core loop is: load a knowledge base, accept an input (webhook or CLI), route it through a chosen model with a system prompt and a fixed set of tools, take one action, and store the result.
agent.json defines the system prompt, the model (OpenAI or Anthropic, selectable), the tools it may call, and the schedule or trigger.
Ingest a local docs/ folder into a SQLite vector table for retrieval; re-index on a command.
Implement exactly two real tools, an HTTP fetch/POST to a named API using credentials from .env, and a write-to-file or write-to-db action; document how to add a third.
Run on a webhook endpoint and on a cron schedule; keep an immutable log of every run: input, retrieved chunks, model, tokens, tool calls, output, and errors.
Add retries with backoff on 429 and 5xx, a per-run token budget cap, and visible failed runs rather than silent drops.
Put all secrets in .env, ship .env.example, and never commit credentials.
Store all data locally by default and make CSV export straightforward.
Include clear empty, loading, success, and recoverable error states in a plain server-rendered run dashboard.
Write focused tests for retrieval, one tool call, and one end-to-end happy path.
Create a README with setup, the agent config schema, how to add a tool, data location, and cost notes.
Do not add accounts, billing, telemetry, a visual builder, or a connector marketplace.
Deliberately leave out multi-tenant workspaces and team permissions.
Deliberately leave out one-click OAuth for dozens of third-party apps.
Finish by running the tests and listing the exact commands used.

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

# FlowHunt indie build

## Goal

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

## Scope

Wire one AI agent to one or two data sources, route a prompt through a chosen model, run a fixed tool/action, and return or store the result on a schedule or 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:
- 100+ maintained connectors and the OAuth apps, token refresh, and schema-drift upkeep behind them
- the no-code visual builder that lets non-developers assemble and edit agents
- one-provider model routing with credit metering across OpenAI, Anthropic, Google, Meta, and Mistral
- managed hosting, execution capacity, and reliability instead of a box you babysit
- team workspaces, shared agents, and role permissions

If those capabilities are essential, use n8n 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 replacement for one FlowHunt agent in an empty repository.
Use TypeScript, Node.js 22, SQLite via better-sqlite3, and the Vercel AI SDK; do not offer alternative stacks.
Scope: a single configurable AI agent, not a general no-code platform. Pick one job and do it well.
The core loop is: load a knowledge base, accept an input (webhook or CLI), route it through a chosen model with a system prompt and a fixed set of tools, take one action, and store the result.
agent.json defines the system prompt, the model (OpenAI or Anthropic, selectable), the tools it may call, and the schedule or trigger.
Ingest a local docs/ folder into a SQLite vector table for retrieval; re-index on a command.
Implement exactly two real tools, an HTTP fetch/POST to a named API using credentials from .env, and a write-to-file or write-to-db action; document how to add a third.
Run on a webhook endpoint and on a cron schedule; keep an immutable log of every run: input, retrieved chunks, model, tokens, tool calls, output, and errors.
Add retries with backoff on 429 and 5xx, a per-run token budget cap, and visible failed runs rather than silent drops.
Put all secrets in .env, ship .env.example, and never commit credentials.
Store all data locally by default and make CSV export straightforward.
Include clear empty, loading, success, and recoverable error states in a plain server-rendered run dashboard.
Write focused tests for retrieval, one tool call, and one end-to-end happy path.
Create a README with setup, the agent config schema, how to add a tool, data location, and cost notes.
Do not add accounts, billing, telemetry, a visual builder, or a connector marketplace.
Deliberately leave out multi-tenant workspaces and team permissions.
Deliberately leave out one-click OAuth for dozens of third-party apps.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- OpenAI or Anthropic API key (and any other model providers you route to)
- API or OAuth credentials for each tool the agent actually touches
- a vector store or SQLite for the knowledge base
- a scheduler or webhook endpoint
- a place to host the runner

## 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.

# FlowHunt product brief

## Problem

You can build one agent; you cannot build FlowHunt. A single-purpose AI agent, take a knowledge base or a data source, route a prompt through a model, take an action, return the result, is a one-sitting build. But the paid product's value sits outside that solo rebuild: a hosted runner that stays up, 100+ maintained connectors with all the OAuth and schema-drift upkeep behind them, one-provider model routing with credit accounting, a no-code visual builder aimed at people who will not write code, and team workspaces. That is integration breadth plus managed infrastructure, which is a structural moat, not polish. The prompt below is the honest consolation build: the one workflow you actually run, self-hosted, and you own its upkeep from then on.

## Product outcome

Wire one AI agent to one or two data sources, route a prompt through a chosen model, run a fixed tool/action, and return or store the result on a schedule or webhook.

## Target user

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

## Required capabilities

- OpenAI or Anthropic API key (and any other model providers you route to)
- API or OAuth credentials for each tool the agent actually touches
- a vector store or SQLite for the knowledge base
- a scheduler or webhook endpoint
- a place to host the runner

## Explicit non-goals for v1

- 100+ maintained connectors and the OAuth apps, token refresh, and schema-drift upkeep behind them
- the no-code visual builder that lets non-developers assemble and edit agents
- one-provider model routing with credit metering across OpenAI, Anthropic, Google, Meta, and Mistral
- managed hosting, execution capacity, and reliability instead of a box you babysit
- team workspaces, shared agents, and role permissions

## 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 replacement for one FlowHunt agent in an empty repository.
Use TypeScript, Node.js 22, SQLite via better-sqlite3, and the Vercel AI SDK; do not offer alternative stacks.
Scope: a single configurable AI agent, not a general no-code platform. Pick one job and do it well.
The core loop is: load a knowledge base, accept an input (webhook or CLI), route it through a chosen model with a system prompt and a fixed set of tools, take one action, and store the result.
agent.json defines the system prompt, the model (OpenAI or Anthropic, selectable), the tools it may call, and the schedule or trigger.
Ingest a local docs/ folder into a SQLite vector table for retrieval; re-index on a command.
Implement exactly two real tools, an HTTP fetch/POST to a named API using credentials from .env, and a write-to-file or write-to-db action; document how to add a third.
Run on a webhook endpoint and on a cron schedule; keep an immutable log of every run: input, retrieved chunks, model, tokens, tool calls, output, and errors.
Add retries with backoff on 429 and 5xx, a per-run token budget cap, and visible failed runs rather than silent drops.
Put all secrets in .env, ship .env.example, and never commit credentials.
Store all data locally by default and make CSV export straightforward.
Include clear empty, loading, success, and recoverable error states in a plain server-rendered run dashboard.
Write focused tests for retrieval, one tool call, and one end-to-end happy path.
Create a README with setup, the agent config schema, how to add a tool, data location, and cost notes.
Do not add accounts, billing, telemetry, a visual builder, or a connector marketplace.
Deliberately leave out multi-tenant workspaces and team permissions.
Deliberately leave out one-click OAuth for dozens of third-party apps.
Finish by running the tests and listing the exact commands used.

## 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 FlowHunt 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