by AutoResearch-Factory · Agent Tool · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/AutoResearch-Factory/Agon.git
git clone https://github.com/AutoResearch-Factory/agon-artifacts.git
Taken from the project's README on GitHub.
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.
| Stars | 52 |
| Forks | 5 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-30 |
| Created | 2026-06-18 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
Agon Claude Code plugin for autonomous AI research — from a bare topic to running experiments, with no human-written experimental code English | 中文 Agon (paper) takes a research project from a one-line topic to running experiments.
These tools work well together with Agon for enhanced workflows:
Looking for a Agon alternative? If you're comparing Agon with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
An open-source research system for autonomous investigation, recursive self-improvement, and human–AI collabor
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
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
Autonomous AI backend for deep research AI applications.
Autonomous multi-branch research lab. Branches compete for compute budget. The system converges on what works.
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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: