agent-mistake-patterns@1.0.0
CAUTION· static onlyPublisher claims: "AI agent mistake pattern vault and self-correction discipline: memory-recall failures (notes exist but not consulted before acting), missing workflow steps, edit-anchor mistakes, inefficient debugging paths. Teaches the pre-action recall habit (grep your own notes first), mistake archiving, root-cause classification, and fix-verification loops. AI智能体犯错模式库与自我纠错纪律：记忆检索失败（笔记在、动手前没翻）、流程步骤缺失、编辑锚点错误、低效排查路径。强调动手前先检索自己的笔记、错误归档、根因分类与修复验证闭环。Keywords: agent mistakes, self-correction, memory recall, note retrieval, error patterns, root cause, 智能体纠错, 犯错模式, 记忆检索, 笔记纪律, 自我改进, error archive, 防错机制" Observed in sandbox: static analysis only — behavior not yet observed.
Coverage
Static only; nothing was run yet.The label rests on the code, the instruction files and the publisher. The honeypot run replaces this verdict when it completes.
Is [email protected] safe to install?
KillerSkills analysed it statically (a sandbox run is pending) on 2026-11-07 and scored it 71/100 (Caution). It is not known to be malicious, but it has behaviour or code a security policy should decide on before install. Summary: static analysis only — behavior not yet observed.
Behavior trace
Queued for honeypot run · static analysis onlyThis version has not been detonated yet. The score above counts behavior as unknown (50/100), so it cannot read Verified until the honeypot run completes and replaces this verdict.
Static findings
No findings across 199 lines in 2 files.
Cost to the agent
Tokens estimated at four characters each. Skills load their instruction text when triggered; the figure is what that costs.
Known vulnerabilities
Vulnerability advisories (OSV) cover npm and PyPI packages; this registry has no advisory database.
What would make this Verified
9 points to 80 · weights: behavior 45%, static 25%, prompt 15%, publisher 15%- Unclaimed publisher≈ +8 on the totalClaim mowenqwq: a verified publisher gets +50 on publisher trust. It is free and usually instant with GitHub sign-in.