research-harness — repository deleted, archived audit

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by Biajin-PKU · MCP Server · ★ 73

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ai-agentscodexliterature-reviewmcpopen-sourceprovenanceresearch-automationresearch-toolsscientific-researchzotero

Quick Facts

Stars73
Forks9
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score61/100
Last Updated2026-06-09
Created2026-04-21
Platformscodex, mcp, python
Est. Tokens~601k

About research-harness

简体中文 · English 做证据可信、过程可追、自主优化的长程自动化 AI 科研工具 Research Harness 是科研 agent 的 harness 层:为 agent 提供证据状态、过程记录、工具接口和反馈回路,让检索、阅读、判断与写作成为可长期运行、可持续积累的自动化科研流程。 适用于 Codex、Claude Code、Gemini CLI、OpenCode、Cline、Cursor Agent、Kilo Code 等主流 AI 编程 agent;也支持通过 agent profile 接入 OpenClaw 等第三方 agent 工具。 当前子系统状态和公开工具面以 docs/STATUS.md 为准。 3 分钟跑通 如果你只想先确认 RH 是否能在本机跑起来,按这个顺序执行: 然后把这段交给 Codex、Claude Code 或其他已安装 RH skills 的...

research-harness alternative? Top 6 similar tools

Looking for a research-harness alternative? If you're comparing research-harness with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • awesome-academic-research-skills by kael-odin · ⭐ 181

    面向中文用户的学术论文与科研 Agent Skill 每日排行榜 · 自动搜索、过滤并排名 GitHub 上的 Claude Code / Codex / OpenCode 科研 Skill 仓库

  • research-hub by WenyuChiou · ⭐ 66

    AI-operable research workspace for Zotero, Obsidian, and NotebookLM. Use any two, or all three, through CLI, M

  • research-agent-skills by KalarisLabs · ⭐ 61

    Turn any AI agent into a research co-scientist: 280+ open-source agent skills for literature reviews, hypothes

  • De-Anthropocentric-Research-Engine by Pthahnix · ⭐ 229

    De-Anthropocentric Research Engine — AI-powered academic research automation with deep literature survey, gap

  • cli-anything-zotero by PiaoyangGuohai1 · ⭐ 122

    CLI server for Zotero 7/8/9 — let AI manage your library. 70+ CLI commands for search, import, PDF, BibTeX, no

  • CoAutoResearch by YihongT · ⭐ 107

    An open-source research system for autonomous investigation, recursive self-improvement, and human–AI collabor

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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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