-mcp-to-skill-converter: Convert any MCP server into a Claude Skill with 90% context savings

by GBSOSS · MCP Server · ★ 159

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars159
Forks18
LanguagePython
CategoryMCP Server
Quality Score72/100
Open Issues3
Last Updated2025-10-26
Created2025-10-26
Platformsclaude-code, mcp, python
Est. Tokens~1k

About -mcp-to-skill-converter

mcp-to-skill-converter Convert any MCP server into a Claude Skill with 90% context savings. Why This Exists MCP servers are great but load all tool definitions into context at startup. With 20+ tools, that's 30-50k tokens gone before Claude does any work.

Compatible Skills

These tools work well together with -mcp-to-skill-converter for enhanced workflows:

  • happy-claude-skills — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (56%)
  • awesome-claude-skills — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (56%)
  • skill-prompt-generator — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (56%)
  • skills — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (56%)
  • dailypaper-skills — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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