autogenstudio-skills: Repo of skills for autogen studio using model context protocol (mcp)

by madtank · MCP Server · ★ 316

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

Security audit verdict: SAFE · quality 76/100

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

Stars316
Forks51
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score76/100
Last Updated2025-01-22
Created2024-01-30
Platformsmcp, python
Est. Tokens~16k

About autogenstudio-skills

AutoGen Studio MCP Tools What is MCP? Model Context Protocol (MCP) is like a USB for AI tools - it provides a standardized way for AI agents to discover and use different capabilities. Think of it as plugging in new abilities for your AI agents! With our flexible dictionary-based implementation, agents can easily discover and use tools without needing to know their implementation details.

Compatible Skills

These tools work well together with autogenstudio-skills for enhanced workflows:

  • investor-agent — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)
  • mcp-memory-service — semantic(0.19)+shared_fw(autogen)+same_lang+similar_pop+shared_platform (50%)
  • autogen-studio — semantic(0.28)+complementary+shared_fw(autogen)+similar_pop (48%)

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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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