MedAgents: [ACL 2024 Findings] MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning

by gersteinlab · Agent Tool · ★ 370

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

Security audit verdict: SAFE · quality 60/100

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.

llmllm-agentllm-agentsmedical-applicationmedical-question-answering

Quick Facts

Stars370
Forks50
LanguagePython
CategoryAgent Tool
Quality Score60/100
Last Updated2024-05-27
Created2023-11-15
Platformspython
Est. Tokens~466k

About MedAgents

[ACL 2024 Findings] MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning 📖 Paper We propose a Multi-disciplinary Collaboration (MC) framework. The framework works in five stages: (i) expert gathering: gather experts from distinct disciplines according to the clinical question; (ii) analysis proposition: domain experts put forward their own analysis with their...

Compatible Skills

These tools work well together with MedAgents for enhanced workflows:

  • CeCeData-Agent — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • OpenClaw-Medical-Skills — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (55%)
  • data-analysis-llm-agent — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • SimplerLLM — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (54%)
  • 4D-ARE — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)

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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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