Skip to main content

MCP server exposing reusable skills for AI coding agents

Project description

DevSkills

An MCP server that brings Anthropic's Agent Skills to any MCP-compatible coding agent.

What this enables: Your team creates a shared repository of skills — development workflows, code reviews,debugging, etc.— and every team member's AI agent (Claude Code, Cursor, Copilot) can use them automatically.

What are Skills?

Skills are Anthropic's concept for giving AI agents specialized knowledge. Instead of repeating context every conversation, you package instructions, scripts, and references into a folder that agents load on-demand.

Think of skills like onboarding docs for a new hire: "Here's how we do deployments. Here's our code review checklist. Here's the security patterns we follow." Except the new hire is an AI agent.

The key design principle is progressive disclosure — agents see only skill names and descriptions upfront, then load full instructions only when relevant. This means you can have dozens of skills without bloating context.

The Problem

Native Skills support exists only in Claude Code, where skills live in ~/.claude/skills/ or .claude/skills/.

Teams using Cursor, GitHub Copilot, or other AI coding tools can't use Skills and can't share a common skill repository across different tools and maybe even agents running on the server side.

How DevSkills Works

DevSkills runs as an MCP server that exposes your skills to any MCP-compatible agent:

┌─────────────────────────────────────────┐
│     devskills (MCP Server)              │
│  ├── bundled_skills/  (defaults)        │
│  └── your skills via --skills-path      │
└─────────────────────────────────────────┘
                    │
                    │ MCP Protocol
                    ▼
┌─────────────────────────────────────────┐
│     AI Coding Agents                    │
│  Claude Code, Cursor, GitHub Copilot    │
└─────────────────────────────────────────┘

How agents use skills:

  1. Discovery — Agent calls list_skills(), sees names and descriptions
  2. Selection — Agent decides which skill matches the user's request
  3. Loading — Agent calls get_skill(name) to load full instructions
  4. Execution — Agent follows the instructions, optionally fetching scripts or references

This mirrors Anthropic's progressive disclosure: metadata first, full content only when needed.

Team workflow:

  1. Team creates a skills repository (manually or via devskills init)
  2. Each developer clones the repo locally
  3. Each developer configures their MCP client to point to the local checkout:
{
  "mcpServers": {
    "devskills": {
      "command": "uvx",
      "args": ["devskills", "--skills-path", "/path/to/team-skills"]
    }
  }
}

Same skills, any agent.

Quick Start

1. Create a Skills Repository

uvx devskills init my-team-skills
cd my-team-skills
git init && git add . && git commit -m "Initial commit"

2. Configure Your MCP Client

Add devskills to your agent's MCP config, pointing to your skills:

{
  "mcpServers": {
    "devskills": {
      "command": "uvx",
      "args": ["devskills", "--skills-path", "/path/to/my-team-skills/skills"]
    }
  }
}

See Setup Guide for agent-specific configuration (Claude Code, Cursor, GitHub Copilot).

Creating Skills

The recommended way to create a skill is using the built-in skill-creator:

I want to create a new skill for code review. Use devskills.

This guides you through creating a skill with the correct structure.

See Creating Skills for the full guide, including skill structure and SKILL.md format.

Documentation

Anthropic Resources:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

devskills-0.1.0.tar.gz (102.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

devskills-0.1.0-py3-none-any.whl (59.6 kB view details)

Uploaded Python 3

File details

Details for the file devskills-0.1.0.tar.gz.

File metadata

  • Download URL: devskills-0.1.0.tar.gz
  • Upload date:
  • Size: 102.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.9.17 {"installer":{"name":"uv","version":"0.9.17","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for devskills-0.1.0.tar.gz
Algorithm Hash digest
SHA256 bd55a465ea88b2b413a416256608983b742a7139707646ab0318a1ac6dbe3547
MD5 1bb033038077600e6d691c875c42a7fb
BLAKE2b-256 39793717dc6ae524918b8afd7c50bcd5455b1d96a549c63b91733bc6bf667555

See more details on using hashes here.

File details

Details for the file devskills-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: devskills-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 59.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.9.17 {"installer":{"name":"uv","version":"0.9.17","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}

File hashes

Hashes for devskills-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 f1966b31d591844d34e55569ca8cd83dc1206ba3caaa995faeb2e0661cc923b6
MD5 04d115801b8bad6535610de01253b909
BLAKE2b-256 a0cc062a1c88121478d8ee6d9c0a4f800f9f01ac676de9df903db5533eeb32aa

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page