De-Anthropocentric-Research-Engine: AI-powered academic research automation with deep literature survey, gap analysis, idea generation, experiment

by Pthahnix · MCP Server · ★ 229

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

How to install De-Anthropocentric-Research-Engine

git clone https://github.com/Pthahnix/De-Anthropocentric-Research-Engine.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 62/100

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.

academic-researchai-agentai-scientistarxivauto-researchclaude-codecodexdeep-researchidea-generatorliterature-review

Quick Facts

Stars229
Forks16
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score62/100
Open Issues3
Last Updated2026-05-15
Created2026-02-10
Platformsclaude-code, codex, mcp, node
Est. Tokens~136k

About De-Anthropocentric-Research-Engine

🧠 DARE — De-Anthropocentric Research Engine Human-centered AI-assisted research can no longer sustain the next great leaps of our civilization. What we need is not just more tools, but an AI researcher that thinks and acts independently — a new entity to replace the human role in science. This is DARE. 🚧 Personal side project. Actively under development.

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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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