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New to FastSkill? Start with the Welcome Page to learn about the problems FastSkill solves and how it expands the Claude Code skill standard.
FastSkill Architecture Overview

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What is FastSkill?

FastSkill is a language-agnostic service layer that enables AI agents and agent frameworks to discover, load, and execute skills directly. It provides comprehensive services that agents integrate with to access skill functionality without requiring a tool calling layer.
FastSkill supports both Rust and Python implementations, allowing you to use the same skill ecosystem regardless of your preferred programming language.

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Key Features

Semantic Search

Find skills using OpenAI embeddings for semantic understanding, not just keyword matching.

Cursor Integration

Automatically generate skill registries for Cursor IDE integration and AI agent tool discovery.

Progressive Loading

Load skill metadata first, then content on demand for optimal performance and memory usage.

Keyword Search

Traditional text-based search for reliable, predictable skill discovery.

Multiple Output Formats

Export results as tables, JSON, or XML for different integration needs.

Local Vector Index

Store embeddings locally in SQLite for fast, offline semantic search.

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Quick Start

Get up and running with FastSkill in under 5 minutes: 
import asyncio
from fastskill import FastSkillService, ServiceConfig

async def main():
    # Create service with default configuration
    service = FastSkillService()
    await service.initialize()

    # Register your first skill
    skill_id = await service.register_skill({
        'id': 'my-skill',
        'name': 'My Custom Skill',
        'description': 'A skill for demonstration',
        'version': '1.0.0',
        'tags': ['demo', 'example'],
        'capabilities': ['text-processing']
    })

    # Discover relevant skills
    skills = await service.discover_skills("text processing")
    print(f"Found {len(skills)} relevant skills")

asyncio.run(main())
Your FastSkill service is now running! Check out the Getting Started guide to learn more.

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Architecture Overview

FastSkill follows a modular architecture designed for scalability and language interoperability:

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Core Components

  • CLI Tool: Command-line interface for indexing and searching skills
  • Vector Index: SQLite-based storage for skill embeddings and metadata
  • Embedding Service: OpenAI integration for semantic text vectorization
  • Search Engine: Hybrid search supporting both semantic and keyword matching
  • Markdown Export: Automatic generation of Cursor-compatible skill registries
  • Python SDK: High-level API for programmatic skill management

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Use Cases

FastSkill is perfect for:
  • AI Agent Development: Enable agents to discover and use skills through direct service integration
  • Service Integration: Provide comprehensive services that agents can use directly
  • Skill Marketplaces: Build platforms where users can share and consume skills
  • Enterprise Integration: Standardize skill access across different agent frameworks
  • Development Workflows: Hot reload skills during development without service restart

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Next Steps

1

Install FastSkill

Choose your preferred implementation and follow the installation guide.
2

Create your first skill

Learn how to define and register skills in the skill management section.
3

Integrate with your agent

Connect FastSkill to your AI agent or framework using our integration tutorials.
4

Explore advanced features

Dive into progressive loading and intelligent routing for optimal performance.
FastSkill focuses on skill discovery and search capabilities. For skill execution, read the generated SKILL.md files directly. FastSkill does not provide execution services.

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Getting Help

Ready to get started? Jump to the Quick Start guide or installation instructions.