Vibecode CodeSpring

You are building a lean indie version of CodeSpring.

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 =====
# CodeSpring indie build

## Goal

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

## Scope

A local app where you map features on a canvas, write notes per feature, and generate a PRD + task list file that you feed to your coding agent.

## 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:
- polished drag-and-drop mind-map canvas
- MCP server integration with Cursor/Claude Code/Codex
- semantic docs search
- hosted kanban sync across devices
- prebuilt skills library

If those capabilities are essential, use excalidraw instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build me a local-first app-planning tool in Next.js.
Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind for UI. No auth, single user, runs on localhost.
Core loop:
1. A canvas page where I can add draggable "feature" nodes (title + free-text notes), connect them with lines, and save the layout to SQLite.
2. A "Generate PRD" button per feature that calls the Anthropic API (key from .env) with the feature notes and produces a structured PRD (problem, scope, out-of-scope, acceptance criteria) saved as a markdown file in /docs.
3. A simple kanban board (To Do / In Progress / Done) auto-seeded with tasks parsed from each generated PRD, editable by drag-and-drop.
4. An "Export" button that zips all PRD markdown files plus a tasks.json for dropping into any coding agent's context folder.
Out of scope: multi-user accounts, real-time collaboration, MCP server, mobile app, billing.
Include a README explaining setup, the .env variable needed, and how to point Claude Code/Cursor at the exported /docs folder.

## Required capabilities

- OpenAI/Anthropic API key

## 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 CodeSpring.

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 =====
# CodeSpring indie build

## Goal

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

## Scope

A local app where you map features on a canvas, write notes per feature, and generate a PRD + task list file that you feed to your coding agent.

## 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:
- polished drag-and-drop mind-map canvas
- MCP server integration with Cursor/Claude Code/Codex
- semantic docs search
- hosted kanban sync across devices
- prebuilt skills library

If those capabilities are essential, use excalidraw instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build me a local-first app-planning tool in Next.js.
Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind for UI. No auth, single user, runs on localhost.
Core loop:
1. A canvas page where I can add draggable "feature" nodes (title + free-text notes), connect them with lines, and save the layout to SQLite.
2. A "Generate PRD" button per feature that calls the Anthropic API (key from .env) with the feature notes and produces a structured PRD (problem, scope, out-of-scope, acceptance criteria) saved as a markdown file in /docs.
3. A simple kanban board (To Do / In Progress / Done) auto-seeded with tasks parsed from each generated PRD, editable by drag-and-drop.
4. An "Export" button that zips all PRD markdown files plus a tasks.json for dropping into any coding agent's context folder.
Out of scope: multi-user accounts, real-time collaboration, MCP server, mobile app, billing.
Include a README explaining setup, the .env variable needed, and how to point Claude Code/Cursor at the exported /docs folder.

## Required capabilities

- OpenAI/Anthropic API key

## 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 CodeSpring.

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 =====
# CodeSpring product brief

## Problem

The core loop, a mind map that turns feature notes into PRDs and kanban tasks fed to a coding agent, is just structured prompting and file generation, well within reach of a one-shot build. What's missing is the polish: the visual mind-map canvas, MCP/CLI integrations with Claude Code, Cursor and Codex, and the docs search. A DIY version gets you most of the value but with a rougher UI and manual glue between the planning docs and your agent of choice.

## Product outcome

A local app where you map features on a canvas, write notes per feature, and generate a PRD + task list file that you feed to your coding agent.

## Target user

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

## Required capabilities

- OpenAI/Anthropic API key

## Explicit non-goals for v1

- polished drag-and-drop mind-map canvas
- MCP server integration with Cursor/Claude Code/Codex
- semantic docs search
- hosted kanban sync across devices
- prebuilt skills library

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

===== ARCHITECTURE.md =====
# Architecture

## Starting brief

Build me a local-first app-planning tool in Next.js.
Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind for UI. No auth, single user, runs on localhost.
Core loop:
1. A canvas page where I can add draggable "feature" nodes (title + free-text notes), connect them with lines, and save the layout to SQLite.
2. A "Generate PRD" button per feature that calls the Anthropic API (key from .env) with the feature notes and produces a structured PRD (problem, scope, out-of-scope, acceptance criteria) saved as a markdown file in /docs.
3. A simple kanban board (To Do / In Progress / Done) auto-seeded with tasks parsed from each generated PRD, editable by drag-and-drop.
4. An "Export" button that zips all PRD markdown files plus a tasks.json for dropping into any coding agent's context folder.
Out of scope: multi-user accounts, real-time collaboration, MCP server, mobile app, billing.
Include a README explaining setup, the .env variable needed, and how to point Claude Code/Cursor at the exported /docs folder.

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

# CodeSpring indie build

## Goal

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

## Scope

A local app where you map features on a canvas, write notes per feature, and generate a PRD + task list file that you feed to your coding agent.

## 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:
- polished drag-and-drop mind-map canvas
- MCP server integration with Cursor/Claude Code/Codex
- semantic docs search
- hosted kanban sync across devices
- prebuilt skills library

If those capabilities are essential, use excalidraw instead of pretending the gap is solved.

# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

# Build plan

## Original build brief

Build me a local-first app-planning tool in Next.js.
Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind for UI. No auth, single user, runs on localhost.
Core loop:
1. A canvas page where I can add draggable "feature" nodes (title + free-text notes), connect them with lines, and save the layout to SQLite.
2. A "Generate PRD" button per feature that calls the Anthropic API (key from .env) with the feature notes and produces a structured PRD (problem, scope, out-of-scope, acceptance criteria) saved as a markdown file in /docs.
3. A simple kanban board (To Do / In Progress / Done) auto-seeded with tasks parsed from each generated PRD, editable by drag-and-drop.
4. An "Export" button that zips all PRD markdown files plus a tasks.json for dropping into any coding agent's context folder.
Out of scope: multi-user accounts, real-time collaboration, MCP server, mobile app, billing.
Include a README explaining setup, the .env variable needed, and how to point Claude Code/Cursor at the exported /docs folder.

## Required capabilities

- OpenAI/Anthropic API key

## 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.

# CodeSpring product brief

## Problem

The core loop, a mind map that turns feature notes into PRDs and kanban tasks fed to a coding agent, is just structured prompting and file generation, well within reach of a one-shot build. What's missing is the polish: the visual mind-map canvas, MCP/CLI integrations with Claude Code, Cursor and Codex, and the docs search. A DIY version gets you most of the value but with a rougher UI and manual glue between the planning docs and your agent of choice.

## Product outcome

A local app where you map features on a canvas, write notes per feature, and generate a PRD + task list file that you feed to your coding agent.

## Target user

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

## Required capabilities

- OpenAI/Anthropic API key

## Explicit non-goals for v1

- polished drag-and-drop mind-map canvas
- MCP server integration with Cursor/Claude Code/Codex
- semantic docs search
- hosted kanban sync across devices
- prebuilt skills library

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

# Architecture

## Starting brief

Build me a local-first app-planning tool in Next.js.
Stack: Next.js + TypeScript, SQLite via better-sqlite3, Tailwind for UI. No auth, single user, runs on localhost.
Core loop:
1. A canvas page where I can add draggable "feature" nodes (title + free-text notes), connect them with lines, and save the layout to SQLite.
2. A "Generate PRD" button per feature that calls the Anthropic API (key from .env) with the feature notes and produces a structured PRD (problem, scope, out-of-scope, acceptance criteria) saved as a markdown file in /docs.
3. A simple kanban board (To Do / In Progress / Done) auto-seeded with tasks parsed from each generated PRD, editable by drag-and-drop.
4. An "Export" button that zips all PRD markdown files plus a tasks.json for dropping into any coding agent's context folder.
Out of scope: multi-user accounts, real-time collaboration, MCP server, mobile app, billing.
Include a README explaining setup, the .env variable needed, and how to point Claude Code/Cursor at the exported /docs folder.

## 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 CodeSpring 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