What can you do with this server?

This server enables AI agents to programmatically manage and execute Python workflow scripts through a comprehensive CRUD interface. * Create workflows: Generate new Python workflow scripts with a name, description, and code that includes a required run(params: dict = None) -> dict function * Execute workflows: Run existing workflows by name with optional parameters and receive structured results * List workflows: View all available workflows with their metadata * Read workflows: Retrieve the source code and details of specific workflows * Update workflows: Modify existing workflows' descriptions and/or code * Delete workflows: Remove workflow scripts from the system All workflows follow a standardized interface pattern for consistent parameter handling and execution.

Which integrations are available for this server?

Enables AI agents to programmatically create, manage, and execute independent Python workflow scripts with full access to the Python ecosystem. Allows posting messages to Slack channels via webhook URLs through executable workflow scripts.

How do I use Workflows MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Workflows MCP Server create a workflow that fetches today's top Hacker News stories and saves them to a JSON file" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Skills MCP Server

License: MIT

A Model Context Protocol (MCP) server that enables AI agents to discover, load, and execute Agent Skills - organized folders of instructions, scripts, and resources that give agents additional capabilities.

Based on the Agent Skills specification.

What are Skills?

Skills are folders containing:

SKILL.md - Instructions and metadata (name, description)
scripts/ - Executable Python scripts
references/ - Additional documentation (loaded on demand)
assets/ - Static resources (templates, data files)

Skills use progressive disclosure to efficiently manage context:

Level 1: Name + description always visible in the skill tool description
Level 2: Full SKILL.md loaded when skill(name) is called
Level 3: Scripts/references loaded when execute_skill_script() or get_skill_resource() is called

Features

Dynamic Skill Discovery: All skill names and descriptions are embedded in the skill tool description
Progressive Loading: Load skill instructions on demand
Script Execution: Run pre-built Python scripts from skills
Resource Access: Load reference docs and assets as needed
Agent Skills Compatible: Follows the open Agent Skills specification

Getting Started

Prerequisites

Python 3.10+
An MCP-compatible client (e.g., Manus, Claude Code, Cursor)

Installation

Clone the repository:
git clone https://github.com/Livus-AI/Skills-MCP.git cd Skills-MCP
Install dependencies:
pip install -e .
Run the server:
skills-mcp

Configuration

Skills Directory: By default, skills are stored in the skills/ directory. You can change this by setting the SKILLS_DIR environment variable.

MCP Tools

The server exposes 3 tools:

Tool	Description
`skill(name)`	Load a skill's full instructions. The tool description dynamically includes ALL skill names and descriptions.
`execute_skill_script(skill_name, script_name, params)`	Execute a Python script from a skill's `scripts/` directory.
`get_skill_resource(skill_name, resource_path)`	Load a specific resource file (reference docs, assets).

How It Works

The skill tool description is dynamically generated to always include the name and description of every available skill. This means:

Agents see all skills immediately - No need to call a "list" function
One call to load - skill("name") loads full instructions
Execute when ready - execute_skill_script() runs scripts

Example Workflow

# Agent reads skill tool description and sees: # - hello-world: A simple example skill... # - slack-message: Post messages to Slack... # Step 1: Load the skill skill("slack-message") # Returns: full instructions, available scripts, resources # Step 2: Execute a script execute_skill_script("slack-message", "post.py", {"channel": "#general", "message": "Hello!"}) # Returns: script output

Creating a Skill

See SKILL_CREATION.md for the complete guide.

Quick Start

Create the directory structure:

skills/ └── my-skill/ ├── SKILL.md # Required: Instructions + metadata ├── scripts/ # Optional: Executable scripts │ └── main.py ├── references/ # Optional: Additional docs │ └── api.md └── assets/ # Optional: Static resources └── template.json

Create SKILL.md with frontmatter:

--- name: my-skill description: What this skill does and when to use it. Include keywords that help agents identify relevant tasks. license: MIT metadata: author: your-name version: "1.0" --- # My Skill ## Overview Brief description of what this skill helps accomplish. ## Available Scripts - `scripts/main.py` - Primary functionality ## How to Use Step-by-step instructions...

Create scripts with the standard format:

import sys import json def run(params: dict = None) -> dict: params = params or {} # Your logic here return {"status": "success", "result": "..."} if __name__ == "__main__": params = {} if len(sys.argv) > 1: params = json.loads(sys.argv[1]) result = run(params) print(json.dumps(result))

Example Skills

This repository includes example skills in the skills/ directory:

hello-world - A simple example demonstrating the skill format
slack-message - Post messages to Slack via webhook

Roadmap

create_skill tool - Create new skills programmatically
execute_code tool - Execute arbitrary Python code with e2b sandboxing
Skill validation and linting
Skill versioning and updates

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

License

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

Install Server

A

security – no known vulnerabilities

A

license - permissive license

A

quality - confirmed to work

How are these scores calculated?

Resources

GitHub Repository

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Related Servers

Workflows MCP Server