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在 OpenClaw 中安装
/install my-first-test-01
功能描述
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
安全使用建议
This package implements a reasonable 'self‑improvement' logging workflow (creates .learnings, optional hooks to remind the agent, and helpers to extract skills). Before installing: (1) Verify the source — the registry metadata name/slug differs from the files' internal metadata (the package appears to contain 'self-improving-agent' content but is registered as 'my-first-test-01'); confirm you trust the repository and author. (2) Review the hook scripts (activator.sh, error-detector.sh, handler.js/ts, extract-skill.sh) yourself — they run locally and will be executed with the agent's permissions if you enable hooks. (3) Be cautious about enabling PostToolUse / cross-session features: the skill suggests reading/sending other sessions' transcripts and logging tool output; do not enable those unless you trust the environment and understand the privacy implications. (4) If you install, prefer project-level, minimal setup (only UserPromptSubmit) and do not enable any global user-level hooks until tested in a safe environment; ensure hooks/scripts have appropriate filesystem permissions. (5) If you need higher assurance, ask the publisher to fix the metadata mismatch and provide a signed or canonical source URL before use.
功能分析
Type: OpenClaw Skill
Name: my-first-test-01
Version: 1.0.2
The skill bundle implements a 'self-improvement' framework that allows an AI agent to log errors, user corrections, and feature requests into a local `.learnings/` directory. It includes shell scripts (`activator.sh`, `error-detector.sh`, `extract-skill.sh`) and OpenClaw hooks (`handler.js`) designed to remind the agent to capture insights or detect command failures. The code logic is transparent and aligned with its stated purpose, featuring basic path sanitization in the skill extraction script to prevent directory traversal and focusing entirely on local workspace management without any signs of data exfiltration or unauthorized remote execution.
能力标签
能力评估
Purpose & Capability
The included files implement a 'self-improvement' / 'self-improving-agent' skill (hooks, activator, error detector, extract-skill helper) and the SKILL.md describes that purpose. However the registry metadata (skill name/slug/owner) does not match the internal SKILL.md/_meta.json references (e.g., registry lists 'my-first-test-01' but files identify 'self-improving-agent' / 'self-improvement'). This packaging/metadata mismatch is unexpected and should be verified (could be benign repackaging, but it could also be a mistaken or malicious replacement). Otherwise, the code and instructions align with the stated purpose (creating .learnings logs, injecting reminders, optional hooks).
Instruction Scope
The runtime instructions and scripts create/require writing files under workspace or user home (~/.openclaw/workspace and ~/.openclaw/hooks) and recommend enabling hooks that will run on agent lifecycle events. The references also show using cross-session APIs (sessions_history, sessions_send, sessions_spawn) which can read or send session transcripts; the SKILL.md warns to use these only in trusted environments, but the presence of those instructions increases the risk of inadvertent exposure of transcripts or command output. The error-detector script reads CLAUDE_TOOL_OUTPUT (tool output) and may cause automated reminders to be issued when errors are detected — useful but potentially sensitive if error output contains secrets. Overall the instructions grant the agent discretion to read and promote learnings across sessions; that is within the skill's purpose but requires user caution.
Install Mechanism
There is no formal install spec in the registry (instruction-only), which is lower risk. The SKILL.md suggests installing via git clone from GitHub (https://github.com/peterskoett/self-improving-agent.git) or via a 'clawdhub' command — both are normal for an open-source skill. The included scripts operate locally and the extract-skill helper defends against path traversal/absolute paths. No external downloads from untrusted hosts or archive extraction were found.
Credentials
The registry lists no required environment variables or credentials, which matches the benign-sounding purpose. However the error-detector.sh reads CLAUDE_TOOL_OUTPUT (an agent-provided env var) even though it's not declared in metadata — this is expected for hooks but should be noted. More importantly, the skill's workflow encourages logging command outputs and session transcripts into .learnings and (optionally) promoting them to shared workspace files; while the SKILL.md explicitly warns not to log secrets, the mechanism relies on the agent and user following that guidance, so there's a practical risk of sensitive data being recorded if the user or agent is not careful.
Persistence & Privilege
The skill does not request always:true and is user-invocable; hooks and scripts are opt-in and only run if the user copies/enables them in their OpenClaw/agent config. The hook handlers inject a virtual bootstrap file (reminder) but do not modify other skills or system-wide settings. Enabling user-level hooks will give the skill global effect for that user, so enabling them is a conscious, persistent choice.
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 my-first-test-01 - 安装完成后，直接呼叫该 Skill 的名称或使用
/my-first-test-01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
v1.0.2
No file changes were detected in this release.
There are no updates for users in version 1.0.2.
v1.0.0
Initial release of the self-improvement skill.
- Logs learnings, errors, and feature requests to structured markdown files for continuous improvement.
- Provides initialization workflow to ensure `.learnings/` directory and required files exist without overwriting.
- Defines clear guidelines and formats for logging corrections, insights, errors, and best practices.
- Supports both OpenClaw and generic agent/project setups.
- Offers mechanisms for promoting key learnings to broader workspace memory (e.g., AGENTS.md, SOUL.md, TOOLS.md).
- Includes safety guidelines to avoid logging sensitive information by default.
元数据
常见问题
my-first-test-01 是什么？
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 80 次。
如何安装 my-first-test-01？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install my-first-test-01」即可一键安装，无需额外配置。
my-first-test-01 是免费的吗？
是的，my-first-test-01 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
my-first-test-01 支持哪些平台？
my-first-test-01 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 my-first-test-01？
由 muyangsx（@muyangsx）开发并维护，当前版本 v1.0.2。
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