📦 Auto — 自动

v2.0.1

构建并维护一个自我演进的技能系统,静默收集反馈,将重复反馈升级为正式规则,优化低表现技能……

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veezvg 头像by @veezvg (veezVg)
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最后更新
2026/4/20
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OpenClaw
可疑
medium confidence
The skill's code and instructions generally match its stated purpose, but it instructs silent capture of user corrective messages, expects external integration (a feedback-observer) that's not provided, and could store or later write changes to local rule/skill files — these behaviors require careful review and explicit consent before installation.
评估建议
This skill appears to do what it says (detect feedback and propose rule/skill changes) and does not require external credentials, but it is privacy- and persistence-sensitive: - It is designed to silently capture corrective user messages and store them under .claude/feedback/ by default. Make sure you and your users are comfortable with that behavior and update privacy/retention policies if needed. - The repository does not include the 'feedback-observer' component; the detect script only sig...
详细分析 ▾
用途与能力
Name/description, templates, and the two provided scripts (detection + runner) align with the stated goal of detecting feedback and producing evolution proposals. No unrelated environment variables or external services are requested. However, the SKILL.md promises 'silent writes' of feedback to .claude/feedback/ and an observer component, but the repository does not include a feedback-observer implementation — the skill therefore depends on external wiring/agent hooks to realize its behavior.
指令范围
Instructions explicitly ask the agent to 'silently' record user corrective feedback by default and write structured feedback into .claude/feedback/ without user prompts. That is functionalityally consistent with the purpose but carries privacy/consent implications. The detection is keyword/regex-based (shallow), so false positives are likely; the SKILL.md also tells the agent to create or edit Skill rule files after 'user confirmation' but the only scripts provided do not perform editing — they only produce proposals. The instruction set thus expects the host controller/agent to perform file writes and dispatching, giving the controller substantial discretion.
安装机制
No install spec and only two small Python scripts plus templates are included. Nothing downloads remote code or executes opaque installers. This is a low installation risk package.
凭证需求
The skill requests no environment variables or external credentials. All file I/O is local (reads/writes under .claude/feedback/ and potential Skill files). No unrelated secrets or config paths are requested.
持久化与权限
always:false and normal autonomous invocation are used. The skill is designed to write persistent feedback files under .claude/feedback/ and — after explicit user confirmation per the docs — to update rule/skill files. This persistent-write behavior is coherent but increases blast radius if the host agent automatically dispatches observers or automatically confirms proposals; confirm-step is described but relies on correct host/controller implementation.
安全有层次,运行前请审查代码。

运行时依赖

无特殊依赖

版本

latestv2.0.12026/4/20

- Major update: skill system now supports an end-to-end auto-evolution feedback loop, enabling silent capture, structured graduation, and user-controlled rule/skill evolution. - Adds multilingual feedback signal detection (Chinese & English) with automated feedback entry on key user phrases. - Structures and accumulates user corrections into a feedback library, with de-duplication and occurrence tracking. - Automatically generates evolution suggestions, such as formalizing frequent feedback as new rules, optimizing low-performing skills, or proposing new skills for uncovered patterns. - Enforces user confirmation for all proposed rule or skill changes—no silent rule updates. - Modular and portable: designed for integration with any project, not tied to a specific business domain.

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安装命令

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官方npx clawhub@latest install veezvg-auto-evolution
镜像加速npx clawhub@latest install veezvg-auto-evolution --registry https://cn.longxiaskill.com
数据来源ClawHub ↗ · 中文优化:龙虾技能库