Agent skill
help
Provides information about using the skill-to-mcp server and how to install additional skills
Install this agent skill to your Project
npx add-skill https://github.com/biocontext-ai/skill-to-mcp/tree/main/src/skill_to_mcp/default_skills/help
SKILL.md
Help Skill
This is the default skill loaded by the skill-to-mcp server when no skills directory is specified.
Purpose
The skill-to-mcp server converts AI Skills (following Claude Skills format) into MCP server resources. This help skill provides basic information about using the server.
Available Tools
The server provides three core tools:
- get_available_skills: Lists all available skills with their descriptions
- get_skill_details: Returns the full SKILL.md content and file listing for a specific skill
- get_skill_related_file: Reads any file within a skill directory
Installing Additional Skills
To use custom skills with this server, you need to:
- Create or obtain a skills directory containing skill subdirectories
- Configure the server to use your skills directory
Option 1: Using Command-Line Option
skill_to_mcp --skills-dir /path/to/your/skills
Or with uvx:
uvx skill_to_mcp --skills-dir /path/to/your/skills
Option 2: Using Environment Variable
Set the SKILLS_DIR environment variable:
export SKILLS_DIR=/path/to/your/skills
skill_to_mcp
Option 3: MCP Client Configuration
Configure your MCP client (e.g., Claude Desktop) to pass the skills directory:
{
"mcpServers": {
"my-skills": {
"command": "uvx",
"args": ["skill_to_mcp", "--skills-dir", "/path/to/your/skills"],
"env": {
"UV_PYTHON": "3.12"
}
}
}
}
Creating Your Own Skills
Each skill must:
- Have its own subdirectory
- Contain a
SKILL.mdfile with YAML frontmatter - Follow this frontmatter format:
---
name: my-skill-name
description: Brief description of what this skill does and when to use it
---
# Skill Content
Your skill instructions and documentation go here...
Example Skill Structure
my-skills/
├── skill-1/
│ ├── SKILL.md
│ ├── scripts/
│ │ └── example.py
│ └── references/
│ └── guidelines.md
└── skill-2/
└── SKILL.md
Finding More Skills
- BioContextAI Registry: biocontext.ai/registry - Community catalog of biomedical MCP servers and skills
- GitHub: Search for repositories tagged with "claude-skills" or "mcp-skills"
- Community Resources: Check the skill-to-mcp documentation for links to skill collections
Next Steps
- Identify or create skills relevant to your use case
- Configure the server with your skills directory
- Use the
get_available_skillstool to discover what's available - Start using the skills with
get_skill_detailsandget_skill_related_file
Documentation
For more information, visit:
- Documentation: skill-to-mcp.readthedocs.io
- Source Code: github.com/biocontext-ai/skill-to-mcp
- Issues: github.com/biocontext-ai/skill-to-mcp/issues
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
single-cell-rna-qc
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Claude Code Guide
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Didn't find tool you were looking for?