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Welcome to FastSkill

FastSkill is a skills registry and management system that transforms how you discover, install, and manage AI agent skills. Built on the Anthropic/Claude Code skill standard, FastSkill brings the power of modern package managers (like Poetry and npm) to the world of AI agent development.

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The Problem We’re Solving

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The Challenge of Managing AI Agent Skills

As AI agents become more sophisticated, they rely on a growing ecosystem of skills—specialized capabilities that enable agents to perform specific tasks. However, managing these skills at scale presents several challenges: 1. Skill Discovery is Fragmented
  • Skills are scattered across repositories, wikis, and documentation
  • No centralized way to discover what skills are available
  • No standard way to search by capability or use case
  • Hard to know which skills work well together
2. Version Control is Non-Existent
  • Skills evolve over time, but there’s no way to track versions
  • No way to ensure reproducible agent configurations
  • No mechanism to update skills safely
  • Can’t lock to specific versions for production deployments
3. Dependency Management is Manual
  • Skills often depend on other skills or tools
  • No automatic dependency resolution
  • Conflicts between skills are discovered only at runtime
  • Manual coordination required across teams
4. Development Workflow is Cumbersome
  • No clear separation between development and production skills
  • No way to test skills in isolation
  • Difficult to share skills across projects
  • No standardized way to package and distribute skills
5. Integration is Complex
  • Each project reinvents skill loading and discovery
  • No standard way to integrate skills into agent frameworks
  • Embedding computation is expensive and repeated across projects
  • No way to share pre-computed embeddings

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How FastSkill Solves These Problems

FastSkill extends the Anthropic/Claude Code skill standard into a comprehensive registry system that brings software development best practices to AI agent skills.

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🔍 Semantic Discovery

Find skills by meaning, not just keywords. FastSkill uses OpenAI embeddings to enable semantic search—find skills by what they do, not just what they’re called. This means you can search for “powerpoint presentation” and find skills that create presentations, even if they don’t use those exact words. 
# Semantic search finds skills by meaning
fastskill search "create data visualizations"
# Returns: chart-generator, dashboard-builder, data-plotter

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📦 Package Management for Skills

Declarative skill management, similar to Cargo for Rust or npm for Node.js. FastSkill uses a unified skill-project.toml file at the project root for all configuration, similar to pyproject.toml and package.json: 
# skill-project.toml - Unified configuration
[dependencies]
web-scraper = { source = "git", url = "https://github.com/org/repo.git", branch = "main" }
pytest = { source = "source", name = "team-tools", skill = "pytest", groups = ["dev"] }
Benefits:
  • ✅ Reproducible Installations: skills.lock preserves exact installs
  • ✅ Group-Based Organization: Separate dev/prod skills like Poetry groups
  • ✅ Version Tracking: Track skill versions and update safely
  • ✅ Unified Configuration: Single file for dependencies and repository settings

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🔄 Lifecycle Management

Complete skill lifecycle from development to production. FastSkill provides commands for every stage of the skill lifecycle: Development: 
# Create skill metadata for authors
fastskill init

# Install in editable mode (symlink for local development)
fastskill add ./my-skill -e --group dev
Production: 
# Install from manifest (reproducible)
fastskill install --without dev

# Update to latest versions
fastskill update

# Show installed skills and dependencies
fastskill show --tree
Registry Management: 
# Add skill repositories
fastskill registry add team-tools --type git-marketplace --url https://github.com/org/skills.git

# Install from source
fastskill install

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📊 Dependency Resolution

Automatic dependency management. Skills can declare dependencies on other skills, and FastSkill automatically resolves them: 
# Project declares dependencies
[dependencies]
data-processor = { source = "git", url = "https://github.com/org/data-processor.git" }
csv-parser = { source = "source", name = "registry", skill = "csv-parser", version = "^1.0" }
data-validator = { source = "source", name = "registry", skill = "data-validator", version = ">=2.0" }
FastSkill will:
  • ✅ Automatically install required dependencies
  • ✅ Detect version conflicts
  • ✅ Resolve dependency trees
  • ✅ Show dependency relationships with fastskill show --tree

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🏗️ Software Development Lifecycle Integration

FastSkill makes skills a first-class part of your development workflow.

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Version Control Integration

Skills are tracked in version control just like code dependencies: 
# Add skills to your project
fastskill add https://github.com/org/skill.git

# Commit manifest and lock files
git add skill-project.toml skills.lock
git commit -m "Add web-scraper skill"

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CI/CD Integration

Reproducible installations make CI/CD pipelines reliable: 
# GitHub Actions example
- name: Install skills
  run: fastskill install --lock

- name: Run agent tests
  run: pytest tests/

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Team Collaboration

Teams can share skill configurations: 
# Developer 1: Adds a skill
fastskill add --group dev pytest

# Developer 2: Installs all skills
fastskill install

# Production: Installs only production skills
fastskill install --without dev

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Environment Management

Different skill sets for different environments: 
[dependencies]
# Development environment gets extra tools
dev-tools = { source = "source", name = "registry", skill = "dev-tools", groups = ["dev"] }

# Production gets monitoring
monitoring = { source = "source", name = "registry", skill = "monitoring", groups = ["prod"] }

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🚀 Performance Optimizations

Pre-computed embeddings and efficient storage. FastSkill includes performance optimizations that make skill management scalable: Pre-computed Embeddings:
  • Skills can bundle pre-computed embeddings in ZIP packages
  • No API calls needed during installation
  • Faster search and discovery
  • Consistent embeddings across installations
Local Vector Storage:
  • SQLite-based embedding database
  • Fast semantic search without external API calls
  • Offline operation capability
  • Incremental indexing for efficiency

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Expanding the Claude Code Standard

FastSkill builds on the solid foundation of the Anthropic Claude Code skill standard (SKILL.md format) and extends it with:

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1. Registry Infrastructure

The Claude Code standard defines how individual skills are structured. FastSkill adds the infrastructure to manage multiple skills:
  • Source Tracking: Know where each skill came from
  • Version Management: Track versions across installations
  • Repository Support: Multiple skill sources (Git, ZIP, local)

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2. Package Management

Skills become manageable packages:
  • Manifest Files: Declarative skill definitions
  • Lock Files: Reproducible installations
  • Dependency Resolution: Automatic dependency management
  • Group Organization: Environment-based skill grouping

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3. Development Workflow

Skills integrate into standard development workflows:
  • Editable Installs: Local development with symlinks
  • Version Control: Track skills in git
  • CI/CD Integration: Reproducible builds
  • Team Collaboration: Shared skill configurations

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4. Lifecycle Management

Complete lifecycle support:
  • Discovery: Semantic search across skills
  • Installation: From various sources (Git, ZIP, repositories)
  • Updates: Safe version updates
  • Removal: Clean skill removal with dependency checks

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Key Features

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📋 Declarative Configuration

Define your skills declaratively in skill-project.toml at your project root: 
# skill-project.toml
[dependencies]
web-scraper = { source = "git", url = "https://github.com/org/repo.git", branch = "main" }
data-processor = { source = "source", name = "team-tools", skill = "data-processor", groups = ["prod"] }

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🔒 Reproducible Installations

Lock files ensure exact reproducibility: 
# skills.lock - Exact installed state
[[skills]]
id = "web-scraper"
version = "1.2.3"
commit_hash = "abc123def456"
fetched_at = "2024-01-01T12:00:00Z"

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🏷️ Group-Based Organization

Organize skills by environment: 
# Install all skills
fastskill install

# Install without dev group
fastskill install --without dev

# Install only production
fastskill install --only prod

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🔗 Editable Installs

Develop skills locally with symlink support: 
# Install in editable mode
fastskill add ./local-skill -e --group dev

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📚 Source Management

Manage multiple skill repositories: 
# Add a repository
fastskill registry add team-tools --repo-type git-marketplace https://github.com/org/skills.git

# List repositories
fastskill registry list

# Install from source
fastskill install
Find skills by meaning: 
# Search by capability
fastskill search "create presentations"

# Find visualization tools
fastskill search "charts and graphs"

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Use Cases

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Individual Developers

Manage your personal skill library:
  • Track skills you use across projects
  • Version control your skill configurations
  • Update skills safely
  • Discover new skills by capability

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Teams

Share skill configurations:
  • Standardize skill sets across team members
  • Separate development and production skills
  • Track skill updates in version control
  • Collaborate on skill selection

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Organizations

Enterprise skill management:
  • Central skill repositories
  • Version control and auditing
  • Environment-specific skill sets
  • Dependency management at scale

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Skill Authors

Publish and distribute skills:
  • Create versioned skill packages
  • Include pre-computed embeddings
  • Define dependencies
  • Distribute via Git, ZIP, or repositories

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Getting Started

Ready to get started? Here’s a quick path:
  1. Install FastSkill - Get FastSkill set up in your environment
  2. Quick Start Guide - 5-minute setup tutorial
  3. Core Concepts - Understand skills, manifests, and sources
  4. CLI Reference - Complete command documentation

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Why FastSkill?

FastSkill brings software engineering best practices to AI agent skills:
  • ✅ Reproducibility: Lock files ensure consistent installations
  • ✅ Scalability: Manage hundreds of skills across projects
  • ✅ Collaboration: Share skill configurations with teams
  • ✅ Reliability: Version tracking and dependency resolution
  • ✅ Performance: Pre-computed embeddings and efficient storage
  • ✅ Standards-Based: Built on Claude Code skill standard

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What’s Next?

FastSkill: Making AI agent skills manageable, versioned, and reproducible—just like modern software packages.