← 返回 Skills 市场
Self-Improving Engineering
作者
José I. O.
· GitHub ↗
· v1.2.1
· MIT-0
100
总下载
0
收藏
0
当前安装
3
版本数
在 OpenClaw 中安装
/install self-improving-engineering
功能描述
Captures architecture decisions, code quality issues, build/deploy failures, dependency problems, performance regressions, tech debt accumulation, and test g...
安全使用建议
This skill appears coherent and low-risk for its purpose, but review before enabling hooks: 1) Only enable the hooks you want (activator/UserPromptSubmit recommended). 2) Inspect scripts (activator.sh, error-detector.sh, extract-skill.sh) to confirm behavior and file paths. 3) If you enable PostToolUse/error-detector, be cautious because CLAUDE_TOOL_OUTPUT may contain sensitive command output—ensure the detector is configured to only emit short, redacted reminders. 4) Prefer installing from a trusted repository or vendor; if the source is unknown, review the full code locally before copying to ~/.openclaw or enabling hooks.
功能分析
Type: OpenClaw Skill
Name: self-improving-engineering
Version: 1.2.1
The skill bundle is a legitimate engineering tool designed to help AI agents log and promote architectural decisions, build failures, and technical debt. It consists of markdown templates, shell scripts for scaffolding (extract-skill.sh), and OpenClaw hooks (handler.js/ts) that inject reminders into the agent's context. The scripts include basic security checks against path traversal, and the instructions explicitly advise the agent against logging sensitive information like secrets or private keys.
能力标签
能力评估
Purpose & Capability
Name/description, README instructions, scripts, and hook handlers all implement a logging/prompting workflow to capture engineering learnings (.learnings, promotion to workspace docs). No unrelated credentials, binaries, or network endpoints are requested.
Instruction Scope
Runtime instructions create/ensure .learnings files and recommend installing optional hooks. Hook handlers inject a virtual reminder into agent bootstrap and shell scripts read CLAUDE_TOOL_OUTPUT for simple error-pattern detection. This is consistent with the skill's purpose, but the skill does write files to the workspace/home and (if PostToolUse is enabled) will read tool output—review to ensure no secrets or full stack traces are logged.
Install Mechanism
No automated install spec; install instructions are copy/clone or use clawdhub. All included scripts are local and there are no downloads from untrusted URLs or extract-from-URL steps.
Credentials
The skill declares no required environment variables or credentials. The only environment usage is reading CLAUDE_TOOL_OUTPUT in an optional error-detector script, which is appropriate for a PostToolUse hook but should be enabled only when you trust the runtime and its outputs.
Persistence & Privilege
always:false and hooks are user-enabled; handlers inject virtual files into session context (intended reminder). The skill does not request permanent platform-wide privileges or modify other skills' configs.
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-engineering - 安装完成后，直接呼叫该 Skill 的名称或使用
/self-improving-engineering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
v1.2.1
**Version 1.1.0**
- Added stackability contract for multi-skill installations.
- Added namespaced logging guidance (`.learnings/engineering/`) for coexistence with other skills.
- Added required `Skill: engineering` metadata field and cross-skill precedence/ownership rules.
- Clarified hook arbitration model (single dispatcher, dedupe, rate limiting).
v1.1.0
Version 1.1.0
- Switched to a manual-first workflow for logging engineering learnings and issues.
- Updated documentation to clarify that reminders/hooks are opt-in and not enabled by default.
- Adjusted hook instructions to emphasize lightweight, manual activation for reminders.
- No code or implementation changes; documentation improvements only.
v1.0.0
Initial release of self-improving-engineering skill for continuous engineering improvement.
- Provides structured logging of build failures, architecture decisions, code quality issues, performance regressions, dependency problems, tech debt, and test gaps.
- Introduces standardized markdown log files in a `.learnings/` directory: `LEARNINGS.md`, `ENGINEERING_ISSUES.md`, and `FEATURE_REQUESTS.md`.
- Offers detailed guidance for initializing log files and using the logging workflow across various engineering situations.
- Supports promotion of important learnings to architecture decision records, coding standards, or CI/CD runbooks.
- Includes integration recommendations for OpenClaw workspaces and generic setup instructions for other code agents.
元数据
常见问题
Self-Improving Engineering 是什么？
Captures architecture decisions, code quality issues, build/deploy failures, dependency problems, performance regressions, tech debt accumulation, and test g... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 100 次。
如何安装 Self-Improving Engineering？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install self-improving-engineering」即可一键安装，无需额外配置。
Self-Improving Engineering 是免费的吗？
是的，Self-Improving Engineering 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Self-Improving Engineering 支持哪些平台？
Self-Improving Engineering 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 Engineering？
由 José I. O.（@jose-compu）开发并维护，当前版本 v1.2.1。
推荐 Skills