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Self-Improving Science
作者
José I. O.
· GitHub ↗
· v1.1.0
· MIT-0
105
总下载
0
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install self-improving-science
功能描述
Captures learnings, experiment issues, and methodology corrections for continuous improvement in scientific research and ML workflows. Use when: (1) Data lea...
安全使用建议
This skill appears to do what it says: local reminders and simple, opt-in scripts to log experiment learnings. Before enabling: 1) Review the hook scripts (activator.sh, error-detector.sh) so you’re comfortable they only print reminders and scan local tool output (they do not exfiltrate data). 2) Be mindful when using cross-session features (sessions_history, sessions_send) — those can surface other session transcripts or findings; avoid sending raw sensitive data. 3) Don’t write proprietary data, PII, API keys, or raw datasets into the .learnings/ files; follow the SKILL.md guidance to redact. 4) If you install hooks, prefer the minimal activator-only setup or add matcher filters to reduce noise. 5) If you plan to use the manual git install, inspect the referenced GitHub repo before cloning. If you want a tighter review, provide the remote repository URL or confirm whether the repo owner is trusted.
功能分析
Type: OpenClaw Skill
Name: self-improving-science
Version: 1.1.0
The 'self-improving-science' skill bundle is a legitimate tool designed to help researchers and data scientists log experiment issues, methodology corrections, and feature requests. It includes bash scripts (activator.sh, error-detector.sh, extract-skill.sh) and OpenClaw hooks (handler.js/ts) that facilitate the capture of research metadata and the promotion of learnings into structured artifacts like model cards or checklists. The scripts perform standard file operations with appropriate path validation to prevent directory traversal, and the instructions explicitly advise the agent against logging sensitive data such as API keys or PII.
能力标签
能力评估
Purpose & Capability
Name/description (capture learnings, promote to docs/checklists) align with the provided scripts and hook handlers. Included scripts (activator, error-detector, extract-skill) and hook handlers implement reminders, local error-detection, and skill scaffolding which are coherent for this purpose.
Instruction Scope
Runtime instructions and scripts are scoped to creating/maintaining .learnings/ logs, injecting a virtual reminder on agent bootstrap, and scanning tool output for error patterns. The docs also instruct using OpenClaw inter-session APIs (sessions_history, sessions_send, sessions_spawn) to share learnings across sessions — this is consistent with the skill goal but increases risk of exposing cross-session transcripts or learnings if used carelessly. The error-detector reads the CLAUDE_TOOL_OUTPUT environment variable to look for patterns; it does not transmit that content elsewhere, but it does inspect possibly-sensitive tool output.
Install Mechanism
No install spec is provided (instruction-only install), which is the lowest risk category. Manual install examples reference a GitHub repo URL; no arbitrary downloads, shorteners, or archive extraction are used by the skill itself.
Credentials
The skill declares no required env vars or credentials. One script (error-detector.sh) reads CLAUDE_TOOL_OUTPUT (a runtime variable supplied by the host agent) but that env var is not listed in requires.env — this is expected for hook scripts but worth noting because the script inspects tool output which can contain sensitive information if the agent/tool emits it.
Persistence & Privilege
The skill is opt-in: hooks and scripts must be enabled/installed by the user. always:false and no indication of modifying other skills or system-wide configs. Hook handler injects a virtual reminder file at bootstrap; it does not persist credentials or enable itself without user action.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install self-improving-science - 安装完成后，直接呼叫该 Skill 的名称或使用
/self-improving-science触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.1.0
**Version 1.1.0**
- Added stackability contract for multi-skill installations.
- Added namespaced logging guidance (`.learnings/science/`) for coexistence with other skills.
- Added required `Skill: science` metadata field and cross-skill precedence/ownership rules.
- Clarified hook arbitration model (single dispatcher, dedupe, rate limiting).
v1.0.0
- Initial release of the self-improving-science skill for logging experiment learnings, issues, and methodology corrections in scientific and ML workflows.
- Provides structured Markdown log files (`LEARNINGS.md`, `EXPERIMENT_ISSUES.md`, `FEATURE_REQUESTS.md`) under a `.learnings/` directory.
- Includes setup instructions and quick-reference actions for different experiment situations.
- Supports promotion of key findings to checklists, data governance documents, and model cards.
- Offers integration guidance for OpenClaw and generic agent environments.
元数据
常见问题
Self-Improving Science 是什么？
Captures learnings, experiment issues, and methodology corrections for continuous improvement in scientific research and ML workflows. Use when: (1) Data lea... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 105 次。
如何安装 Self-Improving Science？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install self-improving-science」即可一键安装，无需额外配置。
Self-Improving Science 是免费的吗？
是的，Self-Improving Science 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Self-Improving Science 支持哪些平台？
Self-Improving Science 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Self-Improving Science？
由 José I. O.（@jose-compu）开发并维护，当前版本 v1.1.0。
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