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Skill bjornslib/mcp-to-uber-skills-converter/skills/mcp-to-skill-converter/templates

Registry of MCP-derived skills with progressive disclosure. Use when asked about "github", "assistant-ui", "MCP tools", or any converted MCP server. Provides 90%+ context savings compared to native MCP loading.From its SKILL.md

Install
npx -y skills add bjornslib/mcp-to-uber-skills-converter --skill templates

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 7 commands, including `cat .claude/skills/mcp-skills/<skill-name>/SKILL.md` and 6 more.

SKILL.md

2.7 KB, 672 tokens by cl100k_base, as published. Nobody here has run it

MCP Skills Registry

Central directory for all MCP-derived skills. Each sub-skill wraps an MCP server with progressive disclosure.

Available Skills

SkillToolsTrigger Keywords
<!-- Add skills here after conversion. See mcp-to-skill-converter workflow Step 3. -->

See index.json for the machine-readable list.

⚠️ These Are Skill Wrappers, NOT Native MCP Tools

You CANNOT call mcp__shadcn__*, mcp__github__*, etc. directly - those don't exist. These skills wrap MCP servers via a central executor.py.

Usage

Step 1: Read the skill's SKILL.md (from project root):

cat .claude/skills/mcp-skills/<skill-name>/SKILL.md

# Example: shadcn skill
cat .claude/skills/mcp-skills/shadcn/SKILL.md

Step 2: Use the central executor.py (from project root):

# List available skills
python .claude/skills/mcp-skills/executor.py --skills

# List tools in a skill
python .claude/skills/mcp-skills/executor.py --skill github --list

# Get tool schema
python .claude/skills/mcp-skills/executor.py --skill github --describe create_issue

# Call a tool
python .claude/skills/mcp-skills/executor.py --skill github --call '{"tool": "create_issue", "arguments": {...}}'

Context Efficiency

ScenarioNative MCP (all servers)This RegistrySavings
Idle40-100k tokens~150 tokens99%+
Using 1 skill40-100k tokens~5k tokens90%+
After execution40-100k tokens~150 tokens99%+

How It Works

  1. Registry loads first - This file (~150 tokens)
  2. User requests a tool - e.g., "create a GitHub PR"
  3. Sub-skill loads - Only the relevant skill's SKILL.md (~4k tokens)
  4. Executor runs - External process, 0 context tokens
  5. Result returned - Context drops back to registry only

Adding New Skills

Use the mcp-to-skill-converter skill:

cd .claude/skills/mcp-to-skill-converter
python mcp_to_skill.py --name <server-name>
# Outputs to .claude/skills/mcp-skills/<server-name>/

Skill Structure

Each sub-skill contains:

.claude/skills/mcp-skills/<skill-name>/
├── SKILL.md           # Tool documentation
├── executor.py        # Async MCP client (legacy, use central executor)
├── mcp-config.json    # Server config
└── package.json       # Dependencies

This registry enables progressive disclosure of MCP servers as Claude Skills.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.