research-toolkit: Research methods, workflow tools, and independent review for AI agents, packaged as a Skill, plugin, and local

by rrrrrredy · MCP Server · ★ 292

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

Security audit verdict: SAFE · quality 70/100

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. Full audit →

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.

agent-pluginsagent-skillai-researchdeep-researchindustry-analysisindustry-researchindustry-research-frameworklongform-researchlongform-writingmcp-server

Quick Facts

Stars292
Forks32
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score70/100
Last Updated2026-10-22
Created2026-06-24
Platformsmcp, python
Est. Tokens~19k

About research-toolkit

Research Toolkit English | 简体中文 Research Toolkit is an open-source collection of research methods and tools for AI agents. It includes workflows, instructions, checking scripts, and evaluation materials to support planning, source collection, analysis, review, and evidence-backed writing.

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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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