⚓ SkillPort
The SkillOps Toolkit for Agent Skills
SkillOps = Validate, manage, and deliver skills at scale.
Why SkillPort?
| When you... | SkillPort... |
|-------------|--------------|
| Use a coding agent without native skill support | Serves via MCP or CLI |
| Build your own AI agent | Provides MCP server, CLI, and Python library |
| Have 50+ skills and need the right one fast | Search-first loading (Tool Search Tool pattern) |
| Check skills before deployment | Validates against the spec in CI |
| Manage skill metadata programmatically | Provides meta commands |
| Find a skill on GitHub | Installs with add <url> |
Fully compatible with the Agent Skills specification.
Features
Validate
Check skills against the Agent Skills specification.
skillport validate # Validate all skills
skillport validate ./skills # Validate specific directory
skillport validate --json # CI-friendly JSON output
Catches missing fields, naming issues, and spec violations before they cause problems.
Manage
Full lifecycle management from any source.
# Add from GitHub (shorthand)
skillport add anthropics/skills skills
# Add from GitHub (full URL)
skillport add https://github.com/anthropics/skills/tree/main/skills
# Add from local path or zip
skillport add ./my-skills
skillport add skills.zip
# Update, list, remove
skillport update # Update all from original sources
skillport list # See installed skills
skillport remove <skill-id> # Uninstall
Metadata
Update skill metadata without editing files manually. Useful for automation and keeping skills consistent across a team.
skillport meta get <skill> <key> # Get metadata value
skillport meta set <skill> <key> <val> # Set metadata value
skillport meta unset <skill> <key> # Remove metadata key
Serve
MCP server for clients that don't natively support Agent Skills.
Inspired by Anthropic's Tool Search Tool pattern — search first, load on demand:
| Tool | Purpose |
|------|---------|
| search_skills(query) | Find skills by description (full-text search) |
| load_skill(skill_id) | Get full instructions + path |
Why search matters: With 50+ skills, loading all upfront consumes context and hurts accuracy. SkillPort loads metadata only (~100 tokens/skill), then full instructions on demand.
Works with Cursor, Copilot, Windsurf, Cline, Codex, and any MCP-compatible client.
Quick Start
Install
uv tool install skillport
# or: pip install skillport
Add Skills
# Add from GitHub
skillport add anthropics/skills skills
# Or use a custom skills directory
skillport --skills-dir .claude/skills add anthropics/skills skills
Validate
skillport validate
# ✓ All 5 skill(s) pass validation
Connect to Agents
Choose how to deliver skills to your AI agents:
| Mode | Best for | Setup | |------|----------|-------| | CLI Mode | Agents with shell access (Cursor, Windsurf, Codex, etc.) | Per-project | | MCP Mode | MCP-compatible clients, multi-project | One-time |
CLI Mode
For agents that can run shell commands. No MCP configuration required.
skillport init # Initialize project
skillport doc # Generate AGENTS.md with skill table
skillport show <id> # Load full instructions for a skill
How it works:
skillport docgenerates a skill table in AGENTS.md- The agent reads AGENTS.md to discover available skills
- When needed, the agent runs
skillport show <id>to load full instructions
MCP Mode
For MCP-compatible clients. Install the server:
uv tool install skillport-mcp
Add to your client's config:
{
"mcpServers": {
"skillport": {
"command": "uvx",
"args": ["skillport-mcp"],
"env": { "SKILLPORT_SKILLS_DIR": "~/.skillport/skills" }
}
}
}
Cursor
VS Code / GitHub Copilot
Kiro
CLI Agents
# Codex
codex mcp add skillport -- uvx skillport-mcp
# Claude Code
claude mcp add skillport -- uvx skillport-mcp
Organization
Organize skills with categories and tags. Works with both CLI and MCP modes.
Categories & Tags
Use metadata to organize and filter skills:
# SKILL.md frontmatter
metadata:
skillport:
category: development
tags: [testing, quality]
alwaysApply: true # Always available (Core Skills)
Per-Client Filtering
Expose different skills to different agents:
{
"mcpServers": {
"skillport-dev": {
"command": "uvx",
"args": ["skillport-mcp"],
"env": { "SKILLPORT_ENABLED_CATEGORIES": "development,testing" }
},
"skillport-writing": {
"command": "uvx",
"args": ["skillport-mcp"],
"env": { "SKILLPORT_ENABLED_CATEGORIES": "writing,research" }
}
}
}
Configuration
| Variable | Description | Default |
|----------|-------------|---------|
| SKILLPORT_SKILLS_DIR | Skills directory | ~/.skillport/skills |
| SKILLPORT_ENABLED_CATEGORIES | Filter by category | all |
| SKILLPORT_ENABLED_SKILLS | Filter by skill ID | all |
| SKILLPORT_ENABLED_NAMESPACES | Filter by namespace | all |
| SKILLPORT_CORE_SKILLS_MODE | Core Skills behavior (auto/explicit/none) | auto |
Creating Skills
Create a SKILL.md with YAML frontmatter:
---
name: my-skill
description: What this skill does
metadata:
skillport:
category: development
tags: [example]
---
# My Skill
Instructions for the AI agent.
See Creating Skills Guide for best practices.
Skill Sources
| Source | Features | Target | URL | |--------|----------|--------|-----| | Anthropic Official | Document skills (docx, pdf, pptx, xlsx), design, MCP builder | All users | GitHub | | Awesome Claude Skills | Curated community collection, 2.5k+ stars | Discovery | GitHub | | Hugging Face Skills | Dataset creation, model evaluation, LLM training, paper publishing | ML/AI engineers | GitHub | | Claude Scientific Skills | 128+ scientific skills (bio, chem, ML), 26+ databases | Researchers | GitHub | | ClaudeKit Skills | 30+ skills, auth, multimodal, problem-solving frameworks | Full-stack devs | GitHub | | Superpowers | TDD, debugging, parallel agents, code review workflows | Quality-focused devs | GitHub | | Kubernetes Operations | K8s deployment, monitoring, troubleshooting | DevOps/SRE | GitHub |
Learn More
Development
Status: Work in progress. APIs may change.
git clone https://github.com/gotalab/skillport.git
cd skillport
uv sync
# Run MCP server
SKILLPORT_SKILLS_DIR=.skills uv run skillport-mcp
# Run CLI
uv run skillport --help
# Run tests
uv run pytest
License
MIT