Knowledge Tree Starter Kit

AI-optimized hierarchical documentation with fractal 3-layer structure

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🚀 What Is This?

Knowledge Tree Starter Kit provides a fractal documentation system that enables token-efficient AI context loading.

The Innovation: 3-layer hierarchical documentation

• Strategic (800 tokens) → High-level architecture & decisions
• Tactical (3,200 tokens) → Implementation approaches & modules
• Implementation (18,000 tokens) → Specific code & details

The Impact: 97% token savings - AI can scan 30+ projects in the same token budget as loading 1 full tree.

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💡 Why Token Efficiency Matters

Traditional approach:

Load full codebase → 500,000 tokens → Can't fit in AI context

Fractal approach:

Load strategic layers only → 15,000 tokens → Entire codebase fits!
Need details? → Load tactical/implementation progressively

Real-world benefit: Your AI assistant can understand your entire organization's architecture in a single conversation.

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⚡ Quick Start

1. Install

git clone https://github.com/Mxcks/KT-starter-kit.git
cd kt-starter-kit
pip install -r requirements.txt  # (no dependencies currently!)

2. Create Your First Project

# Initialize a project
python kt.py init my-first-project "Learning Knowledge Tree"

# Add nodes as you work
python kt.py add decision "Use Python for backend" --branch my-first-project \
  --reasoning "Team expertise, great libraries"

python kt.py add commit "Set up project structure" --branch my-first-project

# View your work
python kt.py tree my-first-project

3. Generate Fractal Summaries

# Generate 3-layer summaries
python branches/system-documentation/tools/kt-hierarchical-summarizer.py --branch my-first-project

# Update ISS indexes
python branches/system-documentation/tools/iss-hierarchical-indexer.py

4. Query via AI

# Search for context
python tools/kt-integration/tools/iss-query.py search "python"

# Load strategic layer (97% token savings!)
python tools/kt-integration/tools/iss-query.py layer my-first-project strategic

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📚 Documentation

Doc	Purpose	Time
QUICK-REFERENCE.md	At-a-glance overview + command cheat sheet	5 min
QUICKSTART.md	Detailed getting started guide	15 min
ARCHITECTURE.md	Complete system architecture	30 min
BUILD-PLAN.md	Development roadmap	10 min

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📚 What's Included

🎯 The Fractal Documentation System

Location: branches/system-documentation/

A complete operational branch demonstrating the 3-layer pattern:

• 8 nodes explaining the system
• 4 automation tools included
• Pre-generated ISS summaries
• Meta: The branch uses its own pattern to document itself!

Tools:

1. kt-hierarchical-summarizer.py - Generate 3-layer summaries
2. iss-hierarchical-indexer.py - Update ISS indexes
3. kt-intelligent-loader.py - Smart context discovery
4. kt-tool-node-generator.py - Tool scanning with local LLM

🔧 kt-integration (OpenClaw Skill)

Location: tools/kt-integration/

Query tools for OpenClaw with intelligent automation:

• Query KT index and ISS systems
• Auto-search when planning
• Auto-load strategic layers
• Configurable on/off switches

🗂️ Core KT System

Coming soon: Core KT engine, CLI, branch management

• Tree-based context management
• Branch system for distributed workspaces
• Query engine for cross-project search
• Sync system for team collaboration

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🎓 How To Use

Scenario 1: Planning a New Feature

# AI assistant searches for related work
python tools/kt-integration/tools/kt-smart.py discover --query "authentication API"

# Loads strategic layers of relevant projects
# Shows: "Found 3 related projects: auth-service, api-gateway, user-management"
# Token cost: ~2,400 tokens (vs 75,000 for full trees)

Scenario 2: Code Review

# Load tactical + implementation for specific module
python tools/kt-integration/tools/iss-query.py layer my-project tactical
python tools/kt-integration/tools/iss-query.py layer my-project implementation

# Focused context on just what's being reviewed
# Token cost: ~7,000 tokens (vs 25,000 for full tree)

Scenario 3: Architecture Decision

# Compare strategic layers across multiple projects
python tools/kt-integration/tools/iss-query.py search "database architecture"

# Cross-project architectural analysis
# Token cost: ~5,000 tokens for 5 projects (vs 125,000)

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📊 Token Efficiency Examples

Scenario	Traditional	Fractal	Savings
Understand 1 project	25,000 tokens	800 tokens	97%
Scan 5 projects	125,000 tokens	4,000 tokens	97%
Scan entire codebase (30 projects)	750,000 tokens	24,000 tokens	97%
Planning + deep dive (3 projects)	75,000 tokens	12,000 tokens	84%

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🏗️ Architecture

KT Nodes (JSONL)
    ↓
kt-hierarchical-summarizer.py
    ↓
3-Layer Markdown Summaries
    ├── strategic.md (800 tokens)
    ├── tactical.md (3,200 tokens)
    └── implementation.md (18,000 tokens)
    ↓
iss-hierarchical-indexer.py
    ↓
ISS Indexes (fast lookups)
    ↓
kt-intelligent-loader.py
    ↓
Auto-Discovery & Context Loading

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🎯 Use Cases

For Solo Developers

• Manage multiple projects without losing context
• Progressive learning curve (strategic → tactical → implementation)
• Quick refreshers on old projects

For Teams

• Onboard new members efficiently
• Share architectural decisions
• Maintain consistency across projects
• Cross-project pattern analysis

For AI Assistants

• Understand entire codebases
• Smart context loading
• Token-efficient cross-project analysis
• Progressive detail as needed

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🔧 Configuration

Environment Variables

# Set your KT root
export KT_ROOT=/path/to/kt-starter-kit

# Optional: Custom ISS location
export ISS_ROOT=/custom/path

kt-integration Settings

Edit tools/kt-integration/config/settings.json:

{
  "auto_preload": false,              // Load context on session start
  "auto_search_on_planning": true,    // Auto-search when planning
  "auto_load_strategic": true,        // Auto-load strategic layers
  "quiet_mode": false                 // Silent queries
}

Toggle features:

# Enable smart mode
python tools/kt-integration/tools/kt-config.py smart

# Manual mode only
python tools/kt-integration/tools/kt-config.py manual

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📖 Documentation

• System Documentation Branch - Complete fractal docs guide
• kt-integration Skill - OpenClaw integration
• Publishing Guide - How to customize and publish

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🚧 Roadmap

Phase 1 (Current)

• ✅ Fractal documentation system
• ✅ system-documentation operational branch
• ✅ kt-integration OpenClaw skill
• ✅ ISS integration

Phase 2 (Next)

• Core KT engine integration
• CLI tools (kt.py)
• Branch management system
• More operational branches (templates, examples)

Phase 3 (Future)

• MCP server for universal AI access
• VS Code extension
• Team collaboration features
• CI/CD integrations

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🤝 Contributing

We welcome contributions!

See CONTRIBUTING.md for:

• Ways to contribute (code, docs, examples, bugs)
• Development workflow
• Code style guidelines
• 60+ contribution ideas
• Extension points

Quick ideas:

• Add more examples to QUICKSTART.md
• Create reusable templates
• Build integrations (VS Code, Obsidian)
• Improve error messages
• Add new CLI commands

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📝 License

MIT License - See LICENSE for details

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💬 Community

• Issues: GitHub Issues
• Discord: Join our community (coming soon)
• Docs: Full documentation (coming soon)

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🙏 Credits

Created by: Max Stern
Inspiration: Need for token-efficient AI context in large codebases
Special Thanks: Knowledge Tree community

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⭐ Star This Repo!

If you find this useful, give it a star! It helps others discover token-efficient AI context management.

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Ready to optimize your AI context? Clone and start with strategic layers! 🚀

git clone https://github.com/yourusername/kt-starter-kit.git
cd kt-starter-kit
python tools/kt-integration/tools/kt-smart.py preload