kea-research: Multi-AI collaboration platform that combines responses from multiple AI models, cross-validates information

by KeaBase · Agent Tool · ★ 202

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

How to install kea-research

git clone https://github.com/keabase/kea-research.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 66/100

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.

aianthropicgeminillmlm-studiomistralmulti-agent-systemsollamaopenaiopenrouter

Quick Facts

Stars202
Forks17
LanguageTypeScript
CategoryAgent Tool
Quality Score66/100
Open Issues1
Last Updated2026-01-25
Created2026-01-17
Platformsgemini, node
Est. Tokens~268k

About kea-research

5 AIs. 4 Steps. 1 Answer You Can Trust. Multi-AI collaboration platform that combines responses from multiple AI models, cross-validates information, and delivers verified, consensus-backed answers. The Challenge When you ask a question to different AI models, you often get different answers — each presented with equal confidence.

Compatible Skills

These tools work well together with kea-research for enhanced workflows:

  • Agent-MCP — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • multi-model-chat — semantic(0.27)+rare_topics+same_lang+similar_pop+shared_platform (49%)

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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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