AI Repo Setup

Prepare a repository so AI coding agents can navigate, implement, and verify changes with minimal friction.

Core philosophy: AI agents are new starters with no memory. Every session starts fresh. The codebase itself — not documentation — is the primary context. Only document what is undiscoverable and globally relevant.

Inputs to gather (if missing)

One-sentence project description (what does this project do?)
Functional requirements (what the system should do)
Non-functional requirements (performance, security, scalability constraints)
Business rules (domain logic, validation rules, constraints)
Behavioral preferences for agents (see step 4 for what to ask)

Workflow

1. Analyze existing repo

Read package.json, config files, directory structure
Identify tech stack, package manager, existing scripts
Check for existing AGENTS.md, CLAUDE.md, docs/
Note what's already discoverable from source (don't re-document it)

2. Create or convert `docs/REQUIREMENTS.md`

If docs/REQUIREMENTS.md already exists, ask the user whether to convert it to the status-based format. Preserve all existing content — only restructure the format and add status fields. If creating from scratch, interview user or extract from existing code.

Structure:

# Requirements

## Status Reference

| Status | Meaning |
|--------|---------|
| `draft` | Written but not yet reviewed — may still be vague or incomplete |
| `refined` | Reviewed and clarified by user, ready to be implemented |
| `in-progress` | Actively being implemented by the agent |
| `implemented` | Code written by agent, awaiting user review |
| `verified` | User reviewed and approved — source of truth |
| `deferred` | Intentionally postponed, not abandoned |
| `cancelled` | No longer relevant, kept for historical context |

## Functional Requirements

### [Feature Area]

#### FR-001: [Requirement title]
- **Status**: `draft`
- **Description**: [What the system should do]

#### FR-002: [Requirement title]
- **Status**: `verified`
- **Description**: [What the system should do]

## Non-Functional Requirements

### Performance

#### NFR-001: [Constraint title]
- **Status**: `draft`
- **Description**: [Measurable constraint, e.g. "API responses must be < 200ms at p99"]

### Security

#### NFR-002: [Constraint title]
- **Status**: `draft`
- **Description**: [Constraint]

### Scalability

#### NFR-003: [Constraint title]
- **Status**: `draft`
- **Description**: [Constraint]

Keep requirements specific, testable, and numbered for traceability. The agent sets status to implemented after completing work; the user sets it to verified after review. Never skip verified — implemented means the agent is done, not that the feature is correct.

3. Create or convert `docs/BUSINESS-RULES.md`

If docs/BUSINESS-RULES.md already exists, ask the user whether to convert it to the status-based format. Preserve all existing content — only restructure the format and add status fields. If creating from scratch, interview user or extract from existing code.

Structure:

# Business Rules

## Status Reference

| Status | Meaning |
|--------|---------|
| `draft` | Written but not yet reviewed — may still be vague or incomplete |
| `refined` | Reviewed and clarified by user, ready to be implemented |
| `in-progress` | Actively being implemented by the agent |
| `implemented` | Code written by agent, awaiting user review |
| `verified` | User reviewed and approved — source of truth |
| `deferred` | Intentionally postponed, not abandoned |
| `cancelled` | No longer relevant, kept for historical context |

## [Domain Area]

### BR-001: [Rule name]
- **Status**: `draft`
- **When**: [Trigger condition]
- **Then**: [Expected behavior]
- **Rationale**: [Why this rule exists]

### BR-002: [Rule name]
- **Status**: `verified`
- **When**: [Trigger condition]
- **Then**: [Expected behavior]
- **Rationale**: [Why this rule exists]

Focus on domain logic that isn't obvious from code. Number rules for cross-referencing with tests. Business rules typically reach verified only when both the rule is enforced in code and a test explicitly names the rule ID (e.g. it("BR-001: ...")).

Agent Workflow for Requirements & Business Rules

This is the intended lifecycle for keeping docs in sync with the codebase:

Draft — user or agent adds a new item with status: draft
Refine — agent clarifies the description until it is specific and testable; user confirms; status → refined
Implement — user asks agent to implement a specific ID; agent implements it; status → implemented
Verify — user reviews the implementation; if approved, status → verified; if rejected, status → in-progress with a note

The agent must never set status to verified — only the user does. The agent must update status to implemented before closing a session.

4. Generate minimal `AGENTS.md`

See references/agents-md-guide.md for full principles.

The file must be as small as possible. Only include:

One-sentence project description
Pointers to docs/ with the sync rule (mandatory — without this, the status lifecycle is dead on arrival since agents start fresh every session)
Behavioral instructions (agent workflow preferences that are undiscoverable from source). Ask the user if they have preferences for:
- Plan mode: how agents should present plans (e.g., concise vs detailed, listing unresolved questions)
- Communication style: brevity, formality, language
- Workflow habits: any recurring instructions they find themselves repeating across sessions

Example:

# Project Name

SaaS platform for team retrospectives with real-time collaboration.

## Docs

- `docs/REQUIREMENTS.md` — functional and non-functional requirements
- `docs/BUSINESS-RULES.md` — domain rules and constraints

When implementing features or fixing bugs, update the relevant requirement/rule
status in these docs to keep them synced with the codebase.

## Plan Mode

- Make the plan extremely concise. Sacrifice grammar for the sake of concision.
- At the end of each plan, give me a list of unresolved questions to answer, if any.

Do NOT include: package manager (discoverable from lock files, packageManager field in package.json, or enforcement hooks), commands (discoverable from package.json scripts), architecture descriptions, file listings, framework/library explanations, implementation patterns. These are all discoverable from source.

5. Create `CLAUDE.md` symlink

ln -s AGENTS.md CLAUDE.md

This keeps Claude Code and other tools reading the same instructions.

6. Set up feedback loops (optional)

See references/feedback-loops.md for details.

Ask user which feedback loops to set up:

TypeScript typecheck script in package.json
Test runner (vitest, jest, bun test)
E2E tests (playwright, cypress) — for frontend projects
Pre-commit hooks: Lefthook (recommended) or Husky + lint-staged
Code quality: Oxlint + Oxfmt (recommended), Biome, or ESLint + Prettier

Pre-commit hooks are the most powerful feedback loop for AI agents — they get error messages on failed commits and retry automatically.

7. Set up deterministic enforcement (optional)

See references/deterministic-enforcement.md for details.

Ask user which agent tools they use and set up enforcement for each:

Claude Code: PreToolUse hooks in .claude/settings.json (bash scripts)
OpenCode: plugins in .opencode/plugins/ (TypeScript/JavaScript modules)

Ask about both tools — users may use one or both. Set up enforcement for every tool the user opts into.

Common enforcement rules:

Enforce correct package manager (block npm, yarn, bun, deno if using pnpm)
Block dangerous git commands (git push --force, git reset --hard)
Block specific CLI patterns
Protect sensitive files (.env, credentials)

Enforcement saves instruction budget and is deterministic — rules cannot be ignored by the agent. It also makes the package manager discoverable by agents (they see the error message when blocked), which is why it doesn't need to be in AGENTS.md.

Deliverables

docs/REQUIREMENTS.md — numbered functional + non-functional requirements
docs/BUSINESS-RULES.md — numbered business rules with triggers/behavior
AGENTS.md — minimal, hand-crafted, globally relevant only
CLAUDE.md — symlink to AGENTS.md
Feedback loops configured (if opted in)
Deterministic enforcement configured (if opted in)

Anti-patterns to avoid

Bloated AGENTS.md — every line costs tokens on every session
Documenting the discoverable — agents read package.json scripts, lock files (pnpm-lock.yaml, yarn.lock, etc.), packageManager field, config files, and imports. Don't repeat what they'll find in seconds.
Listing package manager or commands — discoverable from lock files, packageManager field in package.json, and enforcement hooks
File path references — paths change; describe capabilities instead
Auto-generated init files — stale immediately, actively mislead agents
Global rules for local concerns — use progressive disclosure or skills instead
Missing docs sync rule — without telling agents to update requirement/rule statuses, the entire lifecycle system is unused

ai-repo-setup