Project Context Generator For AI

Generates .ai-context knowledge base for coding agents. Activate when: (1) setting up a new project for AI-assisted development, (2) user asks to "create project knowledge" or "setup ai-context", (3) existing .ai-context needs regeneration. Creates tiered documentation structure optimized for agent comprehension and token efficiency.

AI Context Generator

A reusable skill for creating project knowledge bases that help coding agents work faster and smarter.

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🎯 When to Use This Skill

Activate when:

• Setting up a new project for AI-assisted development
• User requests: "create ai-context", "setup project knowledge", "generate .ai-context"
• Existing .ai-context is outdated and needs regeneration
• After major project restructuring

Do NOT activate when:

• Project already has fresh .ai-context (check SKILL.md date)
• User asks for unrelated documentation
• Simple code tasks with clear existing context

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📋 What This Skill Generates

Creates a .ai-context/ directory with:

text

.ai-context/
├── SKILL.md                    # Entry point with activation rules
├── DYNAMICS.md                 # Active issues & constraints (Dynamic)
├── references/
│   ├── PROJECT-ESSENCE.md      # What & why (High stability)
│   ├── ARCHITECTURE.md         # Component relationships (Medium stability)
│   └── DECISIONS.md            # Design decisions (Update on change)
└── meta/
    ├── MAINTENANCE.md          # How to maintain this knowledge
    ├── templates/              # (Optional) Custom templates
    └── scripts/                # (Optional) Maintenance scripts

Stability Tiers

Tier	File	Update Frequency	Token Budget
0	PROJECT-ESSENCE.md	Quarterly / Major version	~500 tokens
1	ARCHITECTURE.md	Monthly / Sprint	~1000 tokens
2	DECISIONS.md	Per decision change	~800 tokens
3	DYNAMICS.md	As needed (issues)	~600 tokens

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🔧 Generation Process

Step 1: Gather Project Intelligence

Before generating, collect:

text

□ Read AGENTS.md (if exists) — operational rules
□ Read README.md — user-facing description
□ Read package.json — dependencies, scripts, entry points
□ Scan directory structure — identify components
□ Read docs/ or litho.docs/ — existing documentation
□ Identify key source files — main entry points
□ Note technology stack — frameworks, languages, platforms

Step 2: Extract Knowledge

For PROJECT-ESSENCE.md:

• What is this project? (one sentence)
• Why does it exist? (problem/solution)
• Who is it for? (target users)
• What does it provide? (key features)
• Core constraints? (security, compatibility)

For ARCHITECTURE.md:

• System diagram (ASCII or Mermaid)
• Component responsibilities
• Data flow between components
• Key dependencies
• Important patterns

For DECISIONS.md:

• Non-obvious design choices
• Trade-offs made
• Constraints accepted
• Decisions that might be revisited

For DYNAMICS.md:

• Current blockers
• Known workarounds
• Temporary constraints
• Recently resolved issues (brief)

Step 3: Generate Files

Use templates from templates/ directory:

1. Start with SKILL.md — entry point with activation rules
2. Generate references/PROJECT-ESSENCE.md — core identity
3. Generate references/ARCHITECTURE.md — component map
4. Generate references/DECISIONS.md — design rationale
5. Generate DYNAMICS.md — active issues
6. Generate meta/MAINTENANCE.md — upkeep guide

Step 4: Validate Quality

text

□ SKILL.md has clear activation triggers
□ PROJECT-ESSENCE.md readable in 2 minutes
□ ARCHITECTURE.md shows big picture (no code)
□ DECISIONS.md justified with rationale
□ DYNAMICS.md only contains current issues
□ All files dated at top
□ Total token budget < 4000 tokens

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📝 Writing Principles

Do:

• ✅ Write for someone who knows nothing about the project
• ✅ Use diagrams over paragraphs
• ✅ Focus on "why" not "how"
• ✅ Keep files under 150 lines each
• ✅ Link between related sections
• ✅ Include "Last updated" dates

Don't:

• ❌ Copy-paste code snippets (link to files instead)
• ❌ Document every file/function
• ❌ Include details that change frequently
• ❌ Duplicate content across files
• ❌ Use jargon without context

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🔄 Integration with AGENTS.md

text

AGENTS.md = "How to work" (commands, style, rules)
.ai-context = "What the project is" (architecture, decisions, issues)

Both should be read at session start. They serve different purposes and should not overlap.

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📚 Template Reference

Templates are provided in templates/:

Template	Purpose
skill.md.tmpl	SKILL.md with placeholder prompts
essence.md.tmpl	PROJECT-ESSENCE.md structure
architecture.md.tmpl	ARCHITECTURE.md with diagram prompts
decisions.md.tmpl	DECISIONS.md with ADR format
dynamics.md.tmpl	DYNAMICS.md with status tracking
maintenance.md.tmpl	MAINTENANCE.md guide

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🛠️ Automation Scripts

Scripts in scripts/ can help with:

Script	Purpose
generate.ts	Interactive generation from templates
check-drift.ts	Compare documented vs actual structure
audit-dynamics.ts	Flag stale issues (>30 days)

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💡 Example Usage

User: "Setup ai-context for my project"

Agent:

1. Activate this skill
2. Read AGENTS.md, README.md, package.json
3. Scan directory structure
4. Generate each file using templates
5. Ask clarifying questions if needed:
   • "What's the main problem this project solves?"
   • "Any non-obvious design decisions I should know about?"
   • "Current blockers or workarounds?"

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⚠️ Important Notes

• Generated knowledge is a starting point, not final truth
• Agent should verify against actual code during first session
• User should review generated content for accuracy
• Schedule regular audits (monthly recommended)

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📖 References

• Agent Skills Specification
• Architecture Decision Records
• Writing Readable Docs

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This skill creates knowledge bases optimized for AI agents. For questions or improvements, see MAINTENANCE.md.