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 webfuse-com · Agent Tool · ★ 2.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-autoresearch safe to install? View the security audit →
🔬 Awesome Autoresearch A curated, high-signal index of autonomous improvement loops, research agents, and descendants inspired by karpathy/autoresearch. Contents 🛠️ General-purpose descendants 🔬 Research-agent systems 💻 Platform ports and hardware forks 🎯 Domain-specific adaptations 📊 Evaluation & benchmarks 📈 Notable use cases and writeups 📚 Related resources 📄 License 🛠️ General-purpose descendants kayba-ai/recursive-improve - Recursive self-improvement framework where agents capture execution traces, analyze failure patterns, and apply targeted fixes with keep-or-revert evaluation. vukrosic/auto-research - Docs-only control plane for an open autonomous AI research lab — fil
| Stars | 2,539 |
| Forks | 193 |
| Category | Agent Tool |
| Quality Score | 52.3815673844076/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-21 |
| Created | 2026-03-20 |
| Platforms | claude-code |
| Est. Tokens | ~15k |
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Open-source autoresearch powered by autonomous coding agents. Run Claude Code, OpenCode, and Codex with gradin
turns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then run
🦞+🔬 NanoResearch: The Autonomous AI Research Assistant
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awesome-autoresearch is A curated list of autonomous improvement loops, research agents, and autoresearch-style systems inspired by Karpathy's autoresearch.. It is categorized as a Agent Tool with 2.5k GitHub stars.
You can find installation instructions and usage details in the awesome-autoresearch GitHub repository at github.com/webfuse-com/awesome-autoresearch. The project has 2.5k stars and 193 forks, indicating an active community.
The top alternatives to awesome-autoresearch on Agent Skills Hub include awesome-autoresearch, awesome-autoresearch, awesome-autoresearch. 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: