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 ruc-datalab · Codex Skill · ★ 53
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SkillAdam safe to install? View the security audit →
SkillAdam: Better Skills for Your AI Agent Give SkillAdam a skill and tell it what you want to improve. It tests your on relevant tasks, learns from the results, and checks proposed changes before updating the file. Supported platforms: Codex · Claude Code · Cursor Agent · GitHub Copilot. Improve skills for your workflow. Describe your goal and provide examples of tasks your skill should handle. Learn from earlier attempts. Use feedback from previous revisions to guide the next improvement and address recurring mistakes. Make measured changes. Adapt the scope of each revision and keep changes that pass evaluation. Stay in your preferred agent. Start an optimization in the agent you already use, with the option to review individual edits. <img src="assets/skilladam-framework.png" alt="SkillAdam framework: task rollouts, optimization memory and adaptive edits, followed by sk
| Stars | 53 |
| Forks | 2 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 69.1241446950447/100 |
| Last Updated | 2026-09-14 |
| Created | 2026-09-06 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
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SkillAdam is SkillAdam: Better Skills for Your AI Agent🚀 Claude Code/Codex/Cursor 插件，一键进化你的skill. It is categorized as a Codex Skill with 53 GitHub stars.
SkillAdam is primarily written in Python. It covers topics such as agent, agent-skills, agentic.
You can find installation instructions and usage details in the SkillAdam GitHub repository at github.com/ruc-datalab/SkillAdam. The project has 53 stars and 2 forks, indicating an active community.
The top alternatives to SkillAdam on Agent Skills Hub include SimpleLLMFunc, Awesome-AI-For-Security, skills. 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: