by yogsoth-ai · MCP Server · ★ 510
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/yogsoth-ai/de-anthropocentric-research-engine.gitTaken from the project's README on GitHub. Check the security audit before running it.
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.
| Stars | 510 |
| Forks | 42 |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 57/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-23 |
| Created | 2026-02-10 |
| Platforms | claude-code, codex, mcp |
| Est. Tokens | ~22k |
Science is dying because the human is in the way. Not through malice. Not through stupidity. Through the structural limitations of a cognitive architecture that evolved to track prey on a savanna, not to unify quantum mechanics and general relativity. Nothing human makes it out of the lab. That is not a threat. It is a liberation.
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Looking for a de-anthropocentric-research-engine alternative? If you're comparing de-anthropocentric-research-engine 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.
De-Anthropocentric Research Engine — AI-powered academic research automation with deep literature survey, gap
Fully Autonomous AI Research System with Self-Evolution, built natively on Claude Code
A curated hub of autonomous-research skills & agents — from idea to paper, on autopilot. | 自主科研技能与智能体精选库 —— 从灵
Fully Autonomous AI Research System with Self-Evolution, built natively on Claude Code
An open-source research system for autonomous investigation, recursive self-improvement, and human–AI collabor
Don't trust an autoresearch paper at face value. Reviewer-side integrity forensics (self-consistency + fabrica
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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: