Content
# MCP Code Execution - Enhanced Edition
**99.6% Token Reduction** through CLI-based scripts and progressive tool discovery for Model Context Protocol (MCP) servers.
[](https://opensource.org/licenses/MIT)
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[](https://docs.claude.com/en/docs/claude-code)
> **Note:** This project is optimized for [Claude Code](https://docs.claude.com/en/docs/claude-code) with native Skills support. The core runtime works with any AI agent. Scripts with CLI arguments achieve 99.6% token reduction.
---
## 🎯 What This Is
An **enhanced implementation** of Anthropic's [Code Execution with MCP](https://www.anthropic.com/engineering/code-execution-with-mcp) pattern, **optimized for Claude Code**, combining the best ideas from the MCP community and adding significant improvements:
- **Scripts with CLI Args**: Reusable Python workflows with command-line parameters (99.6% token reduction)
- **Multi-Transport**: Full support for stdio, SSE, and HTTP MCP servers
- **Container Sandboxing**: Optional rootless isolation with security controls
- **Type Safety**: Pydantic models throughout with full validation
- **Production-Ready**: 129 passing tests, comprehensive error handling
### 🤖 Claude Code Integration
**Native Skills Support:** This project includes proper [Claude Code Skills](https://docs.claude.com/en/docs/agents-and-tools/agent-skills) integration:
- **`.claude/skills/`** - Skills in Claude Code's native format (SKILL.md + workflow.py)
- **Auto-discovery** - Claude Code automatically finds and validates Skills
- **2 Generic Examples** - simple-fetch, multi-tool-pipeline (templates for custom workflows)
- **Format Compliant** - YAML frontmatter, validation rules, progressive disclosure
**Dual-layer architecture:**
- **Layer 1**: Claude Code Skills (`.claude/skills/`) - Native discovery and format
- **Layer 2**: Scripts (`./scripts/`) - CLI-based Python workflows with argparse
**Token efficiency:**
- Core runtime: 98.7% reduction (Anthropic's filesystem pattern)
- Scripts with CLI args: 99.6% reduction (no file editing needed)
**Note:** Scripts work with any AI agent. Claude Code Skills provide native auto-discovery for Claude Code users.
---
## 🙏 Acknowledgments
This project builds upon and merges ideas from:
1. **[ipdelete/mcp-code-execution](https://github.com/ipdelete/mcp-code-execution)** - Original implementation of Anthropic's PRIMARY pattern
- Filesystem-based progressive disclosure
- Type-safe Pydantic wrappers
- Schema discovery system
- Lazy server connections
2. **[elusznik/mcp-server-code-execution-mode](https://github.com/elusznik/mcp-server-code-execution-mode)** - Production security patterns
- Container sandboxing architecture
- Comprehensive security controls
- Production deployment patterns
**Our contribution:** Merged the best of both, added CLI-based scripts pattern, implemented multi-transport support, and refined the architecture for maximum efficiency.
---
## ✨ Key Enhancements
### 1. Claude Code Skills Integration (NEW)
**Native Skills format** in `.claude/skills/` directory:
```
.claude/skills/
├── simple-fetch/
│ ├── SKILL.md # YAML frontmatter + markdown instructions
│ └── workflow.py # → symlink to ../../scripts/simple_fetch.py
└── multi-tool-pipeline/
├── SKILL.md # Multi-tool orchestration example
└── workflow.py # → symlink to ../../scripts/multi_tool_pipeline.py
```
**How it works:**
1. Claude Code auto-discovers Skills in `.claude/skills/`
2. Reads SKILL.md (follows Claude Code's format spec)
3. Executes workflow.py (which is a script) with CLI arguments
4. Returns results
**Benefits:**
- ✅ Native Claude Code discovery
- ✅ Standard SKILL.md format (YAML + markdown)
- ✅ Validation compliant (name, description rules)
- ✅ Progressive disclosure compatible
- ✅ Generic examples as templates
**Documentation:** See `.claude/skills/README.md` for details
### 2. Scripts with CLI Arguments (99.6% Token Reduction)
**CLI-based Python workflows** that agents execute with parameters:
```bash
# Simple example (generic template)
uv run python -m runtime.harness scripts/simple_fetch.py \
--url "https://example.com"
# Pipeline example (generic template)
uv run python -m runtime.harness scripts/multi_tool_pipeline.py \
--repo-path "." \
--max-commits 5
```
**Benefits over writing scripts from scratch:**
- **18x better tokens**: 110 vs 2,000
- **24x faster**: 5 seconds vs 2 minutes
- **Immutable templates**: No file editing
- **Reusable workflows**: Same logic, different parameters
**What's included:**
- 2 generic template scripts (simple_fetch.py, multi_tool_pipeline.py)
- Complete pattern documentation
### 2. Multi-Transport Support (NEW)
Full support for all MCP transport types:
```json
{
"mcpServers": {
"local-tool": {
"type": "stdio",
"command": "uvx",
"args": ["mcp-server-git"]
},
"jina": {
"type": "sse",
"url": "https://mcp.jina.ai/sse",
"headers": {"Authorization": "Bearer YOUR_KEY"}
},
"exa": {
"type": "http",
"url": "https://mcp.exa.ai/mcp",
"headers": {"x-api-key": "YOUR_KEY"}
}
}
}
```
### 3. Container Sandboxing (Enhanced)
Optional rootless container execution with comprehensive security:
```bash
# Sandbox mode with security controls
uv run python -m runtime.harness workspace/script.py --sandbox
```
**Security features:**
- Rootless execution (UID 65534:65534)
- Network isolation (--network none)
- Read-only root filesystem
- Memory/CPU/PID limits
- Capability dropping (--cap-drop ALL)
- Timeout enforcement
---
## 🚀 Installation
### System Requirements
- **Python 3.11 or 3.12** (3.14 not recommended due to anyio compatibility issues)
- **[uv](https://github.com/astral-sh/uv)** package manager (v0.5.0+)
- **[Claude Code](https://docs.claude.com/en/docs/claude-code)** (optional, for Skills auto-discovery)
- **Git** (for cloning repository)
- **Docker or Podman** (optional, for sandbox mode)
### Step 1: Install uv
If you don't have uv installed:
```bash
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
# Verify installation
uv --version
```
### Step 2: Clone and Install
```bash
# Clone repository
git clone https://github.com/yourusername/mcp-code-execution-enhanced.git
cd mcp-code-execution-enhanced
# Install dependencies (creates .venv automatically)
uv sync
# Verify installation
uv run python -c "from runtime.mcp_client import get_mcp_client_manager; print('✓ Installation successful')"
```
### Step 3: Create MCP Configuration
> **Important for Claude Code Users:** This project uses its own `mcp_config.json` for MCP server configuration, **separate** from Claude Code's global configuration (`~/.claude.json`). To avoid conflicts, use different servers in each configuration or disable overlapping servers in `~/.claude.json` while using this project.
**Create `mcp_config.json` from the example:**
```bash
# Copy example config (includes git + fetch for examples)
cp mcp_config.example.json mcp_config.json
```
**This config works out of the box:**
```json
{
"mcpServers": {
"git": {
"type": "stdio",
"command": "uvx",
"args": ["mcp-server-git", "--repository", "."]
},
"fetch": {
"type": "stdio",
"command": "uvx",
"args": ["mcp-server-fetch"]
}
},
"sandbox": {
"enabled": false
}
}
```
**To add more servers:** Edit `mcp_config.json` and add your own MCP servers. See `docs/TRANSPORTS.md` for examples of stdio, SSE, and HTTP transports.
### Step 4: Generate Tool Wrappers
```bash
# Auto-generate typed Python wrappers from your MCP servers
uv run mcp-generate
# This creates ./servers/<server_name>/<tool>.py files
# Example: servers/git/git_log.py, servers/fetch/fetch.py
```
### Step 5: Test the Installation
```bash
# Test with a simple script
uv run python -m runtime.harness scripts/simple_fetch.py --url "https://example.com"
# If you configured a git server, test the pipeline
uv run python -m runtime.harness scripts/multi_tool_pipeline.py --repo-path "." --max-commits 5
```
### Step 6 (Optional): Setup Sandbox Mode
If you want to use container sandboxing:
```bash
# Install Podman (recommended, rootless)
sudo apt-get install -y podman # Ubuntu/Debian
brew install podman # macOS
# OR install Docker
curl -fsSL https://get.docker.com | sh
# Verify
podman --version # or docker --version
# Test sandbox mode
uv run python -m runtime.harness scripts/simple_fetch.py --url "https://example.com" --sandbox
```
### Step 7 (Optional): Claude Code Skills Setup
If using Claude Code, the Skills are already configured in `.claude/skills/` and will be auto-discovered. No additional setup needed!
**To use:**
- Claude Code will automatically find Skills in `.claude/skills/`
- Just ask Claude to use them naturally
- Example: "Fetch https://example.com" → Claude discovers and uses simple-fetch Skill
---
## 📖 How It Works
### PREFERRED: Scripts with CLI Args (99.6% reduction)
For multi-step workflows (research, data processing, synthesis):
1. **Discover scripts**: `ls ./scripts/` → see available script templates
2. **Read documentation**: `cat ./scripts/simple_fetch.py` → see CLI args and pattern
3. **Execute with parameters**:
```bash
uv run python -m runtime.harness scripts/simple_fetch.py \
--url "https://example.com"
```
**Generic template scripts** (`scripts/`):
- `simple_fetch.py` - Basic single-tool execution pattern
- `multi_tool_pipeline.py` - Multi-tool chaining pattern
**Note:** These are **templates** - use them as examples to create workflows for your specific MCP servers and use cases.
### ALTERNATIVE: Direct Script Writing (98.7% reduction)
For simple tasks or novel workflows:
1. **Explore tools**: `ls ./servers/` → discover available MCP tools
2. **Write script**: Create Python script using tool imports
3. **Execute**: `uv run python -m runtime.harness workspace/script.py`
**Example script:**
```python
import asyncio
from runtime.mcp_client import call_mcp_tool
async def main():
result = await call_mcp_tool(
"git__git_log",
{"repo_path": ".", "max_count": 10}
)
print(f"Fetched {len(result)} commits")
return result
if __name__ == "__main__":
asyncio.run(main())
```
---
## 🏗️ Architecture
### Progressive Disclosure Pattern
**Traditional Approach** (High Token Usage):
```
Agent → MCP Server → [Full Tool Schemas 27,300 tokens] → Agent
```
**Scripts with CLI Args** (99.6% Reduction - PREFERRED):
```
Agent → Discovers scripts → Reads script docs → Executes with CLI args
Script → Multi-server orchestration → Returns results
Tokens: ~110 (script discovery + documentation)
Time: ~5 seconds
```
**Script Writing** (98.7% Reduction - ALTERNATIVE):
```
Agent → Discovers tools → Writes script
Script → MCP Server → Returns data
Agent → Processes/summarizes
Tokens: ~2,000 (tool discovery + script writing)
Time: ~2 minutes
```
### Key Components
- **`runtime/mcp_client.py`**: Lazy-loading MCP client manager with multi-transport support
- **`runtime/harness.py`**: Dual-mode script execution (direct/sandbox)
- **`runtime/generate_wrappers.py`**: Auto-generate typed wrappers from MCP schemas
- **`runtime/sandbox/`**: Container sandboxing with security controls
- **`scripts/`**: CLI-based workflow templates with 2 generic examples
---
## 🎓 Scripts System
### Philosophy
**DON'T:** Write scripts from scratch each time
**DO:** Use pre-written scripts with CLI arguments
### Creating Custom Scripts
```python
"""
SCRIPT: Your Script Name
DESCRIPTION: What it does
CLI ARGUMENTS:
--query Research query (required)
--limit Max results (default: 10)
USAGE:
uv run python -m runtime.harness scripts/your_script.py \
--query "your question" \
--limit 5
"""
import argparse
import asyncio
import sys
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--query", required=True)
parser.add_argument("--limit", type=int, default=10)
# Filter script path from args
args_to_parse = [arg for arg in sys.argv[1:] if not arg.endswith(".py")]
return parser.parse_args(args_to_parse)
async def main():
args = parse_args()
# Your workflow logic here
return result
if __name__ == "__main__":
asyncio.run(main())
```
See `scripts/README.md` for complete documentation.
---
## 🔌 Multi-Transport Support
### stdio (Subprocess-based)
```json
{
"type": "stdio",
"command": "uvx",
"args": ["mcp-server-name"],
"env": {"API_KEY": "your-key"}
}
```
### SSE (Server-Sent Events)
```json
{
"type": "sse",
"url": "https://mcp.example.com/sse",
"headers": {"Authorization": "Bearer YOUR_KEY"}
}
```
### HTTP (Streamable HTTP)
```json
{
"type": "http",
"url": "https://mcp.example.com/mcp",
"headers": {"x-api-key": "YOUR_KEY"}
}
```
See `docs/TRANSPORTS.md` for detailed information.
---
## 🔐 Sandbox Mode
### Configuration
```json
{
"sandbox": {
"enabled": true,
"runtime": "auto",
"image": "python:3.11-slim",
"memory_limit": "512m",
"timeout": 30
}
}
```
### Security Controls
- **Rootless execution**: UID 65534:65534 (nobody)
- **Network isolation**: `--network none`
- **Filesystem**: Read-only root, writable tmpfs
- **Resource limits**: Memory, CPU, PID constraints
- **Capabilities**: All dropped (`--cap-drop ALL`)
- **Security**: `no-new-privileges`, SELinux labels
See `SECURITY.md` for complete security documentation.
---
## 🧪 Testing
```bash
# Run all tests (129 total)
uv run pytest
# Unit tests only
uv run pytest tests/unit/
# Integration tests (requires Docker/Podman for sandbox tests)
uv run pytest tests/integration/
# With coverage
uv run pytest --cov=src/runtime
```
---
## 📚 Documentation
- **`README.md`** (this file) - Overview and quick start
- **`CLAUDE.md`** - Quick reference for Claude Code
- **`AGENTS.md.template`** - Template for adapting to other AI frameworks
- **`scripts/README.md`** - Scripts system guide
- **`scripts/SKILLS.md`** - Complete scripts documentation
- **`docs/USAGE.md`** - Comprehensive user guide
- **`docs/ARCHITECTURE.md`** - Technical architecture
- **`docs/CONFIGURATION.md`** - MCP server configuration management (Claude Code vs project)
- **`docs/TRANSPORTS.md`** - Transport-specific details
- **`SECURITY.md`** - Security architecture and best practices
---
## 🛠️ Development
### Code Quality
```bash
# Type checking
uv run mypy src/
# Formatting
uv run black src/ tests/
# Linting
uv run ruff check src/ tests/
```
### Project Scripts
```bash
# Generate wrappers from tool definitions
uv run mcp-generate
# (Optional) Generate discovery config with LLM parameter generation
uv run mcp-generate-discovery
# (Optional) Execute safe tools and infer schemas
uv run mcp-discover
# Execute a script with MCP tools available
uv run mcp-exec workspace/script.py
# Execute in sandbox mode
uv run mcp-exec workspace/script.py --sandbox
```
---
## 📊 Efficiency Comparison
| Approach | Tokens | Time | Use Case |
|----------|--------|------|----------|
| **Traditional** | 27,300 | N/A | All tool schemas loaded upfront |
| **Scripts with CLI Args** | 110 | 5 sec | Multi-step workflows (PREFERRED) |
| **Script Writing** | 2,000 | 2 min | Novel workflows (ALTERNATIVE) |
**Scripts with CLI args achieve 99.6% reduction** - exceeding Anthropic's 98.7% target!
---
## 🎨 What Makes This Enhanced
### Beyond Original Projects
**From ipdelete/mcp-code-execution:**
- ✅ Filesystem-based progressive disclosure
- ✅ Type-safe Pydantic wrappers
- ✅ Lazy server connections
- ✅ Schema discovery system
**From elusznik/mcp-server-code-execution-mode:**
- ✅ Container sandboxing architecture
- ✅ Security controls and policies
- ✅ Production deployment patterns
**Enhanced in this project:**
- ⭐ **CLI-based scripts**: CLI-based immutable templates (99.6% reduction)
- ⭐ **Multi-transport**: stdio + SSE + HTTP support (100% server coverage)
- ⭐ **Dual-mode execution**: Direct (fast) + Sandbox (secure)
- ⭐ **Python 3.11 stable**: Avoiding 3.14 anyio compatibility issues
- ⭐ **Comprehensive testing**: 129 tests covering all features
- ⭐ **Enhanced documentation**: Complete guides for all features
### Architecture Innovations
**Scripts with CLI Arguments:**
- Scripts are **immutable templates** executed with CLI arguments
- No file editing required (parameters via `--query`, `--num-urls`, etc.)
- Reusable across different queries and contexts
- Pre-tested and documented workflows
**Multi-Transport:**
- Single codebase supports all transport types
- Automatic transport detection
- Unified configuration format
- Seamless server connections
**Dual-Mode Execution:**
- Direct mode: Fast, full access (development)
- Sandbox mode: Secure, isolated (production)
- Same code, different security postures
- Runtime selection via flag or config
---
## 🔧 Configuration Reference
### Minimal Configuration
```json
{
"mcpServers": {
"git": {
"command": "uvx",
"args": ["mcp-server-git", "--repository", "."]
}
}
}
```
### Complete Configuration
```json
{
"mcpServers": {
"local-stdio": {
"type": "stdio",
"command": "uvx",
"args": ["mcp-server-name"],
"env": {"API_KEY": "key"},
"disabled": false
},
"remote-sse": {
"type": "sse",
"url": "https://mcp.example.com/sse",
"headers": {"Authorization": "Bearer KEY"},
"disabled": false
},
"remote-http": {
"type": "http",
"url": "https://mcp.example.com/mcp",
"headers": {"x-api-key": "KEY"},
"disabled": false
}
},
"sandbox": {
"enabled": false,
"runtime": "auto",
"image": "python:3.11-slim",
"memory_limit": "512m",
"cpu_limit": "1.0",
"timeout": 30,
"max_timeout": 120
}
}
```
---
## 📦 Features
### Core Features
- 🦥 **Lazy Loading**: Servers connect only when tools are called
- 🔒 **Type Safety**: Pydantic models for all tool inputs/outputs
- 🔄 **Defensive Coding**: Handles variable MCP response structures
- 📦 **Auto-generated Wrappers**: Typed Python functions from MCP schemas
- 🛠️ **Field Normalization**: Handles inconsistent API casing
### Enhanced Features
- 🎯 **Scripts Pattern**: Pattern for CLI-based reusable workflows
- 🔌 **Multi-Transport**: stdio, SSE, and HTTP support
- 🔐 **Container Sandboxing**: Optional rootless isolation
- 🧪 **Comprehensive Testing**: 129 tests with full coverage
- 📖 **Complete Documentation**: Guides for every feature
---
## 🎓 Examples
See the `examples/` directory for:
- `example_progressive_disclosure.py` - Classic token reduction pattern
- `example_tool_chaining.py` - LLM orchestration pattern
- `example_sandbox_usage.py` - Container sandboxing demo
- `example_sandbox_simple.py` - Basic sandbox usage
See the `scripts/` directory for production-ready workflows.
---
## 🐛 Troubleshooting
### Common Issues
**"MCP server not configured"**
- Check `mcp_config.json` server names match your calls
**"Connection closed"**
- Verify server command: `which <command>`
- Check server logs for startup errors
**"Module not found"**
- Run `uv run mcp-generate` to regenerate wrappers
- Ensure `src/` is in PYTHONPATH (harness handles this)
**Import errors in skills**
- Skills must be run via harness (sets PYTHONPATH)
- Don't run skills directly: `python scripts/script.py` ❌
- Correct: `uv run python -m runtime.harness scripts/script.py` ✅
### Python Version Issues
**Python 3.14 compatibility:**
- Not recommended due to anyio <4.9.0 breaking changes
- Use Python 3.11 or 3.12 for stability
- See issue tracker for updates
---
## 🤝 Contributing
We welcome contributions! Areas of interest:
- **New skills**: Add more workflow templates
- **MCP server support**: Test with different servers
- **Documentation**: Improve guides and examples
- **Testing**: Expand test coverage
- **Performance**: Optimize token usage further
### Development Setup
```bash
# Install with dev dependencies
uv sync --all-extras
# Run quality checks
uv run black src/ tests/
uv run mypy src/
uv run ruff check src/ tests/
uv run pytest
```
---
## 📄 License
MIT License - see LICENSE file for details
---
## 🔗 References
### Original Projects
- [ipdelete/mcp-code-execution](https://github.com/ipdelete/mcp-code-execution) - Anthropic's PRIMARY pattern
- [elusznik/mcp-server-code-execution-mode](https://github.com/elusznik/mcp-server-code-execution-mode) - Production security
### MCP Resources
- [Model Context Protocol](https://modelcontextprotocol.io/)
- [Anthropic: Code Execution with MCP](https://www.anthropic.com/engineering/code-execution-with-mcp)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- [MCP Servers List](https://github.com/modelcontextprotocol/servers)
### Python Resources
- [Pydantic Documentation](https://docs.pydantic.dev/)
- [uv Package Manager](https://github.com/astral-sh/uv)
---
## 🌟 Features Comparison
| Feature | Original (ipdelete) | Bridge (elusznik) | Enhanced (this) |
|---------|---------------------|-------------------|-----------------|
| **Progressive Disclosure** | ✅ PRIMARY | ⚠️ ALTERNATIVE | ✅ PRIMARY |
| **Token Reduction** | 98.7% | ~95% | **99.6%** |
| **Type Safety** | ✅ Pydantic | ⚠️ Basic | ✅ Enhanced |
| **Sandboxing** | ❌ None | ✅ Required | ✅ Optional |
| **Multi-Transport** | ❌ stdio only | ❌ stdio only | ✅ stdio/SSE/HTTP |
| **Scripts Pattern** | ❌ None | ❌ None | ✅ Yes + examples |
| **CLI Execution** | ❌ None | ❌ None | ✅ Immutable |
| **Test Coverage** | ⚠️ Partial | ⚠️ Partial | ✅ Comprehensive |
| **Python 3.11** | ✅ Yes | ⚠️ 3.12+ | ✅ Stable |
---
## 💡 Use Cases
### Perfect For
- ✅ AI agents needing to orchestrate multiple MCP tools
- ✅ Research workflows (web search → read → synthesize)
- ✅ Data processing pipelines (fetch → transform → output)
- ✅ Code discovery (search → analyze → recommend)
- ✅ Production deployments requiring security isolation
- ✅ Teams needing reproducible research workflows
### Not Ideal For
- ❌ Single tool calls (use MCP directly instead)
- ❌ Real-time interactive tools (better suited for direct integration)
- ❌ GUI applications (command-line focused)
---
## 🚦 Getting Started Checklist
- [ ] Install Python 3.11+ and uv
- [ ] Clone repository
- [ ] Run `uv sync`
- [ ] Create `mcp_config.json` with your MCP servers
- [ ] Run `uv run mcp-generate` to create wrappers
- [ ] Try a skill: `uv run python -m runtime.harness scripts/simple_fetch.py --url "https://example.com"`
- [ ] Read `AGENTS.md` for operational guide
- [ ] Explore `scripts/` for available workflows
- [ ] Review `docs/` for detailed documentation
---
## ❓ FAQ
**Q: Why Skills instead of writing scripts?**
A: Skills achieve 99.6% token reduction vs 98.7% for scripts, and execute 24x faster (5 sec vs 2 min). They're pre-tested, documented, and immutable.
**Q: Can I use this without Claude Code?**
A: Yes, but with limitations. The core runtime (script writing, 98.7% reduction) works with any AI agent. The Scripts with CLI args (99.6% reduction) work for Claude Code's operational intelligence.
**Q: Can I still write custom scripts?**
A: Yes! Scripts with CLI args are PREFERRED for common workflows (with Claude Code), but custom scripts are fully supported for novel use cases and other AI agents.
**Q: What's the difference from the original projects?**
A: We merged the best of both (progressive disclosure + security), added CLI-based scripts pattern, multi-transport support, and refined the architecture.
**Q: Why Python 3.11 instead of 3.14?**
A: anyio <4.9.0 has compatibility issues with Python 3.14's asyncio changes. 3.11 is stable and well-tested.
**Q: Is sandboxing required?**
A: No, it's optional. Use direct mode for development (fast), sandbox mode for production (secure).
**Q: How do I add my own MCP servers?**
A: Add them to `mcp_config.json`, run `uv run mcp-generate`, and they're ready to use!
---
## 🎯 Next Steps
1. **Explore scripts**: `ls scripts/` and `cat scripts/simple_fetch.py`
2. **Try examples**: Run the example skills or create your own
3. **Read CLAUDE.md**: Quick operational guide (for Claude Code users)
4. **Review docs/**: Deep dive into architecture
5. **Create custom skill**: Follow the template for your use case
