GoSkills - Claude Skills Management Tool

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A powerful command-line tool to parse, manage, and execute Claude Skill packages. GoSkills is designed according to the specifications found in the official Claude documentation.

Features

• Skill Management: List, search, parse, and inspect Claude skills from local directories
• Runtime Execution: Execute skills with LLM integration (OpenAI, Claude, and compatible APIs)
• Built-in Tools: Shell commands, Python execution, file operations, web fetching, and search
• MCP Support: Model Context Protocol (MCP) server integration
• Dual CLI Tools: Separate tools for skill management and execution
• Comprehensive Testing: Full test suite with coverage reports

Installation

From Source

git clone https://github.com/smallnest/goskills.git
cd goskills
make

Using Homebrew

brew install smallnest/goskills/goskills

Or:

# add tap
brew tap smallnest/goskills

# install goskills
brew install goskills

Quick Start

# Set your OpenAI API key
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"

# List available skills
./goskills-cli list ./skills

# Run a skill
./goskills run "create a react component for a todo app"

Built-in Tools

GoSkills includes a comprehensive set of built-in tools for skill execution:

• Shell Tools: Execute shell commands and scripts
• Python Tools: Run Python code and scripts
• File Tools: Read, write, and manage files
• Web Tools: Fetch and process web content
• Search Tools: Wikipedia and Tavily search integration
• MCP Tools: Integration with Model Context Protocol servers

CLI Tools

GoSkills provides two command-line tools for different purposes:

1. Skill Management CLI (goskills-cli)

Located in cmd/goskills-cli, this tool helps you inspect and manage your local Claude skills.

Building goskills-cli

make cli
# or
go build -o goskills-cli ./cmd/goskills-cli

Available Commands

list

Lists all valid skills in a given directory.

./goskills-cli list ./testdata/skills

parse

Parses a single skill and displays a summary of its structure.

./goskills-cli parse ./testdata/skills/artifacts-builder

detail

Displays the full, detailed information for a single skill, including the complete body content.

./goskills-cli detail ./testdata/skills/artifacts-builder

files

Lists all the files that make up a skill package.

./goskills-cli files ./testdata/skills/artifacts-builder

search

Searches for skills by name or description within a directory. The search is case-insensitive.

./goskills-cli search ./testdata/skills "web app"

2. Skill Runner CLI (goskills)

Located in cmd/goskills, this tool simulates the Claude skill-use workflow by integrating with Large Language Models (LLMs) like OpenAI's models.

Building goskills runner

make runner
# or
go build -o goskills ./cmd/goskills

Available Commands

run

Processes a user request by first discovering available skills, then asking an LLM to select the most appropriate one, and finally executing the selected skill by feeding its content to the LLM as a system prompt.

Requires the OPENAI_API_KEY environment variable to be set.

# Example with default OpenAI model (gpt-4o)
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
./goskills run "create an algorithm that generates abstract art"

# Example with a custom OpenAI-compatible model and API base URL using environment variables
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export OPENAI_API_BASE="https://qianfan.baidubce.com/v2"
export OPENAI_MODEL="deepseek-v3"
./goskills run "create an algorithm that generates abstract art"

# Example with a custom OpenAI-compatible model and API base URL using command-line flags
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
./goskills run --model deepseek-v3 --api-base https://qianfan.baidubce.com/v2 "create an algorithm that generates abstract art"

# Example with a custom OpenAI-compatible model and API base URL using command-line flags, auto-approve without human-in-the-loop
./goskills run --auto-approve --model deepseek-v3 --api-base https://qianfan.baidubce.com/v2 "使用markitdown 工具解析网页 https://baike.baidu.com/item/%E5%AD%94%E5%AD%90/1584"

# Example with a custom OpenAI-compatible model and API base URL using command-line flags, in a loop mode and not exit automatically
./goskills run --auto-approve --model deepseek-v3 --api-base https://qianfan.baidubce.com/v2 --skills-dir=./testdata/skills "使用markitdown 工具解析网 页 https://baike.baidu.com/item/%E5%AD%94%E5%AD%90/1584" -l

Development

Make Commands

The project includes a comprehensive Makefile for development tasks:

# Help - shows all available commands
make help

# Build
make build          # Build both CLI and runner
make cli            # Build CLI only
make runner         # Build runner only

# Testing
make test           # Run all tests
make test-race      # Run tests with race detector
make test-coverage  # Run tests with coverage report
make benchmark      # Run benchmarks

# Code Quality
make check          # Run fmt-check, vet, and lint
make fmt            # Format all Go files
make vet            # Run go vet
make lint           # Run golangci-lint

# Dependencies
make deps           # Download dependencies
make tidy           # Tidy and verify dependencies

# Other
make clean          # Clean build artifacts
make install-tools  # Install development tools
make info           # Display project information

Running All Tests

# Run comprehensive test suite
make test-coverage

# Run specific tool tests
cd tool && ./test_all.sh

Configuration

Environment Variables

• OPENAI_API_KEY: OpenAI API key for LLM integration
• OPENAI_API_BASE: Custom API base URL (optional)
• OPENAI_MODEL: Custom model name (optional)
• TAVILY_API_KEY: Tavily search API key
• MCP_CONFIG: Path to MCP configuration file

MCP Integration

Configure Model Context Protocol (MCP) servers by creating a mcp.json file:

{
  "mcpServers": {
    "server-name": {
      "command": "path/to/server",
      "args": ["arg1", "arg2"]
    }
  }
}

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (make test)
5. Run linting (make check)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.