No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by wanshuiyin · MCP Server · ★ 157
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Anti-Autoresearch safe to install? View the security audit →
Anti-Autoresearch 🛡️ · · · · · · · · 🔬 The field has tolerated unreliable autoresearch long enough — Anti-Autoresearch is the read that finally catches it. 天下苦 autoresearch 久矣 —— Anti-Autoresearc
| Stars | 157 |
| Forks | 8 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.4427701536319/100 |
| Last Updated | 2026-09-28 |
| Created | 2026-06-26 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~22k |
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Anti-Autoresearch is Don't trust an autoresearch paper at face value. Reviewer-side integrity forensics (self-consistency + fabrication), deterministic verdict. 61 signals: 46 integrity hack-patterns (families A–H, verdic. It is categorized as a MCP Server with 157 GitHub stars.
Anti-Autoresearch is primarily written in Python. It covers topics such as ai-generated-content, ai-research, ai-scientist.
You can find installation instructions and usage details in the Anti-Autoresearch GitHub repository at github.com/wanshuiyin/Anti-Autoresearch. The project has 157 stars and 8 forks, indicating an active community.
Anti-Autoresearch is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Anti-Autoresearch on Agent Skills Hub include Overture, ResearchClawBench, de-anthropocentric-research-engine. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: