Create State

# Create State Commit Workflow

## After Commits - MANDATORY (Every Single Time)

After EVERY commit (Git, Mercurial, SVN, or any VCS), IMMEDIATELY call BOTH:

### 1. captureConversationContext

Include:
- Commit hash/ID and message (if available)
- Summary of what was changed and why
- Files modified
- Key decisions made during the work

### 2. captureCode for EACH significant file changed

- New functions, classes, routes, or components
- Call MULTIPLE times for multiple significant files
- Include the actual code, not just descriptions

## CRITICAL

Both captures are REQUIRED. Context without code is incomplete.

```
Every commit = captureConversationContext + captureCode(s)
```

NO EXCEPTIONS.

DO NOT report success to the user until captures are complete.

## Example Workflow

After running: `git commit -m "feat: add user authentication"`

### Step 1: Capture Context

Call `captureConversationContext` with:

```
context: "Commit abc123: feat: add user authentication

## Summary
Added JWT-based authentication with bcrypt password hashing.

## Files Modified
- src/auth/auth_service.py (new)
- src/auth/jwt_handler.py (new)
- src/web/routes.py (updated)
- tests/test_auth.py (new)

## Key Decisions
- Chose JWT over session tokens for stateless auth
- Used refresh tokens for better security
- bcrypt with cost factor 12 for password hashing"
```

### Step 2: Capture Code (for each significant file)

Call `captureCode` for auth_service.py:

```
code: [the actual auth service code]
language: python
file_path: src/auth/auth_service.py
description: JWT authentication service with login, register, and token refresh
change_type: new
ai_model: Claude Opus 4
```

Call `captureCode` for jwt_handler.py:

```
code: [the actual JWT handler code]
language: python
file_path: src/auth/jwt_handler.py
description: JWT token creation and validation utilities
change_type: new
ai_model: Claude Opus 4
```

## Why This Matters

The knowledge graph preserves:
- **What** changed (the code itself)
- **Why** it changed (the context and decisions)
- **How** it evolved (version history)

Future AI sessions can then:
- Understand architectural decisions
- Avoid re-making the same mistakes
- Build on existing patterns
- Maintain consistency across the codebase

## Automatic Synthesis

After every 5 captures, the system automatically synthesizes knowledge:
- Creates comprehensive project summary
- Includes architecture, key files, tech stack, priorities, issues
- Retrieved automatically by `getProjectWorldModel`