Mameyugo AI Context Generator AI Docs Generator

AI Docs Generator

Analyzes a software project and generates structured documentation for AI agents: an AGENT.md entry point plus a .ai/ folder with specialized guides.

No assumptions allowed: every file must reflect what actually exists in the codebase. Mark anything unclear with > ⚠️ Assumption: or > ℹ️ Pending manual completion.

Workflow

Phase 1 — Detect the Tech Stack

Scan these indicators:

Backend: composer.json, requirements.txt, go.mod, package.json → dominant extensions in src/ or app/ → framework signatures (Symfony, Laravel, Django, FastAPI, Spring Boot, Express, Gin, Actix…) → language version (require.php, .nvmrc, go.mod, pyproject.toml).

Frontend: package.json devDependencies (React, Vue, Angular, Alpine…) → config files (vite.config.*, webpack.config.*, tailwind.config.*) → templates (.twig, .blade.php, .jsx, .vue, .svelte) → CSS libs (Bootstrap, Tailwind, AdminLTE, Bulma).

Database / Persistence:

• SQL files, migrations/, ORM detection (Doctrine, Eloquent, Prisma, SQLAlchemy, GORM)
• docker-compose.yml DB image, .env.example vars
• mameyugo/jsonq — check composer.json for "mameyugo/jsonq", then look for data/, storage/json/, db/json/ with *.json collections and an indexes/ subfolder. See references/jsonq.md for full detection rules and DATABASE.md template.

DevOps: Dockerfile, docker-compose.yml, .github/workflows/*.yml, Makefile, justfile, bin/, scripts/, platform files (serverless.yml, render.yaml, fly.toml).

Phase 2 — Detect Architectural Patterns

Map the real folder structure:

Also identify: dependency injection patterns, self-validating Value Objects, repository abstractions, application services vs domain services.

Phase 3 — Detect Real Conventions

Read config files directly:

• .editorconfig → indentation, line endings
• .prettierrc / .php-cs-fixer.php / .eslintrc → code style
• .commitlintrc.json / commitlint.config.js → commit format
• phpstan.neon → static analysis level
• .env.example → real environment variables

Files to Generate

Generate in this priority order (critical first):

1. AGENT.md — project root entry point
2. .ai/ARCHITECTURE.md
3. .ai/SECURITY.md
4. .ai/CONVENTIONS.md
5. .ai/TESTING.md
6. .ai/DATABASE.md
7. .ai/DESIGN-SYSTEM.md
8. .ai/I18N.md
9. .ai/DOCUMENTATION.md
10. .ai/TOOLS.md
11. .ai/ROADMAP.md
12. .ai/GLOSSARY.md
13. .ai/DECISIONS/[YYYY-MM-DD]-001-[title].md — one ADR per key decision detected

For each file's template, read the corresponding reference:

Efficiency tip: read only the reference files needed based on the detected stack. For a pure frontend project, skip references/database.md and references/jsonq.md.

Integration with Existing Docs

Never delete or rename existing files. Instead:

Document tool compatibility in AGENT.md:

## AI Tool Compatibility
- **GitHub Copilot**: `.github/copilot-instructions.md` + `.ai-docs/*.md`
- **Cursor**: `.cursorrules` or `.ai/*.md`
- **Gemini / Claude**: This `AGENT.md` + `.ai/*.md`

Output Summary

After generating all files, output:

## ✅ Generated Documentation

### Detected Stack
- **Primary language**: [...]
- **Framework**: [...]
- **DB / Persistence**: [...]
- **Testing**: [...]
- **DevOps**: [...]

### Generated Files
- [x] `AGENT.md`
- [x] `.ai/ARCHITECTURE.md`
... (list all)

### ADRs Generated
- [x] `.ai/DECISIONS/[date]-001-[decision].md`

### Pre-existing Docs
| File | Status |
|------|--------|
| `.ai-docs/*.md` | Referenced from `.ai/` |

### ⚠️ Areas with Insufficient Information
- [ ] **[Area]**: [what's missing and recommendation]

### 🔍 Inconsistencies Detected
- **[Inconsistency]**: [description and recommendation]

## 🚀 Recommended Next Steps
1. Review and adjust generated files
2. Complete sections marked as pending
3. Share with the team for validation
4. Update docs with each significant architectural change

Constraints

• Read-only on source code: never modify existing project files
• Specific over generic: write "PHPUnit 11", not "testing framework"
• Flag assumptions: use > ⚠️ Assumption: for inferred content
• Flag gaps: use > ℹ️ Pending manual completion instead of inventing content
• Flag inconsistencies: if some modules follow a pattern and others don't, document it

Source: mameyugo/ai-context-generator — distributed by TomeVault.