Vibecode GitHub Copilot

You are building a lean indie version of GitHub Copilot.

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 =====
# GitHub Copilot indie build

## Goal

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

## Scope

Install an open-source IDE assistant, connect model APIs or a local model, index the repo, and generate code/edit suggestions.

## 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:
- native GitHub/IDE integration
- fast completions
- model routing
- code review/security features
- managed billing
- team controls

If those capabilities are essential, use Continue 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 coding assistant setup to replace GitHub Copilot. Requirements:

- Do not build or train a model. Set up the open-source Continue extension in
  VS Code, configured against models I control.
- Chat and edit model: Claude or GPT over API, key referenced from my
  environment, never committed. Autocomplete model: a small local coder model
  served by Ollama, so completions are free and work offline.
- Deliver the actual Continue config file with both models wired, plus
  sensible keybindings for inline edit and chat.
- Enable Continue's codebase indexing for repo-aware answers; document where
  the index lives and how to rebuild it.
- Add scripts/ai-review.sh: pipes `git diff` to the API model (key from .env)
  and prints a short pre-commit review.
- Everything local except the chat-model API calls; turn off the extension's
  telemetry in the config.
- Out of scope: writing an editor extension from scratch, GitHub PR-bot
  features, team seat management.
- README: Ollama install and model pull commands, where keys go, and an honest
  note that local autocomplete is slower and dumber than Copilot's, that is
  the trade for free and private.

## Required capabilities

- VS Code/JetBrains extension
- LLM API or local model
- repo indexing
- optional GitHub token

## 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 GitHub Copilot.

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 =====
# GitHub Copilot indie build

## Goal

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

## Scope

Install an open-source IDE assistant, connect model APIs or a local model, index the repo, and generate code/edit suggestions.

## 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:
- native GitHub/IDE integration
- fast completions
- model routing
- code review/security features
- managed billing
- team controls

If those capabilities are essential, use Continue 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 coding assistant setup to replace GitHub Copilot. Requirements:

- Do not build or train a model. Set up the open-source Continue extension in
  VS Code, configured against models I control.
- Chat and edit model: Claude or GPT over API, key referenced from my
  environment, never committed. Autocomplete model: a small local coder model
  served by Ollama, so completions are free and work offline.
- Deliver the actual Continue config file with both models wired, plus
  sensible keybindings for inline edit and chat.
- Enable Continue's codebase indexing for repo-aware answers; document where
  the index lives and how to rebuild it.
- Add scripts/ai-review.sh: pipes `git diff` to the API model (key from .env)
  and prints a short pre-commit review.
- Everything local except the chat-model API calls; turn off the extension's
  telemetry in the config.
- Out of scope: writing an editor extension from scratch, GitHub PR-bot
  features, team seat management.
- README: Ollama install and model pull commands, where keys go, and an honest
  note that local autocomplete is slower and dumber than Copilot's, that is
  the trade for free and private.

## Required capabilities

- VS Code/JetBrains extension
- LLM API or local model
- repo indexing
- optional GitHub token

## 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 GitHub Copilot.

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 =====
# GitHub Copilot product brief

## Problem

You can use open-source editor agents and local/API models, but Copilot's value is editor integration, model routing, GitHub context, completions, and managed usage.

## Product outcome

Install an open-source IDE assistant, connect model APIs or a local model, index the repo, and generate code/edit suggestions.

## Target user

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

## Required capabilities

- VS Code/JetBrains extension
- LLM API or local model
- repo indexing
- optional GitHub token

## Explicit non-goals for v1

- native GitHub/IDE integration
- fast completions
- model routing
- code review/security features
- managed billing
- team controls

## 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 coding assistant setup to replace GitHub Copilot. Requirements:

- Do not build or train a model. Set up the open-source Continue extension in
  VS Code, configured against models I control.
- Chat and edit model: Claude or GPT over API, key referenced from my
  environment, never committed. Autocomplete model: a small local coder model
  served by Ollama, so completions are free and work offline.
- Deliver the actual Continue config file with both models wired, plus
  sensible keybindings for inline edit and chat.
- Enable Continue's codebase indexing for repo-aware answers; document where
  the index lives and how to rebuild it.
- Add scripts/ai-review.sh: pipes `git diff` to the API model (key from .env)
  and prints a short pre-commit review.
- Everything local except the chat-model API calls; turn off the extension's
  telemetry in the config.
- Out of scope: writing an editor extension from scratch, GitHub PR-bot
  features, team seat management.
- README: Ollama install and model pull commands, where keys go, and an honest
  note that local autocomplete is slower and dumber than Copilot's, that is
  the trade for free and private.

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

# GitHub Copilot indie build

## Goal

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

## Scope

Install an open-source IDE assistant, connect model APIs or a local model, index the repo, and generate code/edit suggestions.

## 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:
- native GitHub/IDE integration
- fast completions
- model routing
- code review/security features
- managed billing
- team controls

If those capabilities are essential, use Continue 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 coding assistant setup to replace GitHub Copilot. Requirements:

- Do not build or train a model. Set up the open-source Continue extension in
  VS Code, configured against models I control.
- Chat and edit model: Claude or GPT over API, key referenced from my
  environment, never committed. Autocomplete model: a small local coder model
  served by Ollama, so completions are free and work offline.
- Deliver the actual Continue config file with both models wired, plus
  sensible keybindings for inline edit and chat.
- Enable Continue's codebase indexing for repo-aware answers; document where
  the index lives and how to rebuild it.
- Add scripts/ai-review.sh: pipes `git diff` to the API model (key from .env)
  and prints a short pre-commit review.
- Everything local except the chat-model API calls; turn off the extension's
  telemetry in the config.
- Out of scope: writing an editor extension from scratch, GitHub PR-bot
  features, team seat management.
- README: Ollama install and model pull commands, where keys go, and an honest
  note that local autocomplete is slower and dumber than Copilot's, that is
  the trade for free and private.

## Required capabilities

- VS Code/JetBrains extension
- LLM API or local model
- repo indexing
- optional GitHub token

## 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.

# GitHub Copilot product brief

## Problem

You can use open-source editor agents and local/API models, but Copilot's value is editor integration, model routing, GitHub context, completions, and managed usage.

## Product outcome

Install an open-source IDE assistant, connect model APIs or a local model, index the repo, and generate code/edit suggestions.

## Target user

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

## Required capabilities

- VS Code/JetBrains extension
- LLM API or local model
- repo indexing
- optional GitHub token

## Explicit non-goals for v1

- native GitHub/IDE integration
- fast completions
- model routing
- code review/security features
- managed billing
- team controls

## 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 coding assistant setup to replace GitHub Copilot. Requirements:

- Do not build or train a model. Set up the open-source Continue extension in
  VS Code, configured against models I control.
- Chat and edit model: Claude or GPT over API, key referenced from my
  environment, never committed. Autocomplete model: a small local coder model
  served by Ollama, so completions are free and work offline.
- Deliver the actual Continue config file with both models wired, plus
  sensible keybindings for inline edit and chat.
- Enable Continue's codebase indexing for repo-aware answers; document where
  the index lives and how to rebuild it.
- Add scripts/ai-review.sh: pipes `git diff` to the API model (key from .env)
  and prints a short pre-commit review.
- Everything local except the chat-model API calls; turn off the extension's
  telemetry in the config.
- Out of scope: writing an editor extension from scratch, GitHub PR-bot
  features, team seat management.
- README: Ollama install and model pull commands, where keys go, and an honest
  note that local autocomplete is slower and dumber than Copilot's, that is
  the trade for free and private.

## 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 GitHub Copilot 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 · this prompt is generated from the build plan · improve it via PR