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Self-Improving Robotics
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-robotics
功能描述
Captures robotics autonomy failures, operational incidents, and engineering learnings to enable continuous improvement across perception, localization, plann...
安全使用建议
This skill appears coherent and focused on capturing robotics learnings, but review a few points before installing: 1) Hooks are opt-in and run with your agent's permissions — enable them only if you want reminders injected on bootstrap or PostToolUse. 2) Inspect scripts (activator.sh, error-detector.sh, extract-skill.sh) before enabling to confirm you accept their behavior; error-detector.sh reads the CLAUDE_TOOL_OUTPUT env var (which can contain sensitive output), though it does not transmit that data externally. 3) The manual install suggestion clones a GitHub repo — verify the repo origin/author if you prefer. 4) Ensure .learnings/ files will not inadvertently store secrets, telemetry with credentials, or infrastructure endpoints; follow the SKILL.md guidance to redact sensitive excerpts. 5) If you want lower noise, enable only the UserPromptSubmit activator and skip the PostToolUse detector. Overall, the skill is internally consistent with its stated purpose.
功能分析
Type: OpenClaw Skill
Name: self-improving-robotics
Version: 1.1.0
The bundle is a structured framework for logging robotics-related engineering learnings, incidents, and feature requests into local markdown files. It includes utility scripts (activator.sh, error-detector.sh, extract-skill.sh) and OpenClaw hooks that monitor command output for robotics-specific error patterns (e.g., 'localization drift', 'CAN timeout') to prompt the agent to document them. The scripts are well-documented, use relative paths with basic traversal protections, and perform no unauthorized network activity or sensitive data access.
能力评估
Purpose & Capability
The name/description (capture robotics incidents and promote learnings) matches the files and behavior: markdown templates, logging conventions, a scaffold script to create skills, and hooks/scripts to remind and detect robotics errors. Required env vars and binaries are none, which is proportional for this documentation-and-hook focused skill.
Instruction Scope
Runtime instructions and hooks create/ensure .learnings/ files, inject a reminder into agent bootstrap, and run a CLI helper and an output-pattern detector. These actions are within the stated scope. Notable: scripts/error-detector.sh reads the CLAUDE_TOOL_OUTPUT environment variable (platform-provided command output) to detect error terms — the script does not echo that output verbatim but will emit a reminder if patterns match. The SKILL.md does explicitly warn not to log secrets.
Install Mechanism
This is instruction-only with no install spec. The repository suggests optional manual git clone from a GitHub URL and enabling hooks; there are no automatic downloads, package installs, or archive extraction in the bundle. The included scripts and hook code are local and self-contained.
Credentials
The skill declares no required environment variables or credentials. The only environment interaction is that error-detector.sh reads CLAUDE_TOOL_OUTPUT (expected for a PostToolUse hook). That access is reasonable for detecting robotics error terms, but users should be aware CLAUDE_TOOL_OUTPUT may contain sensitive command output and the README already flags that as sensitive.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges. Hooks are opt-in: the user must copy/enable hooks under ~/.openclaw/hooks or add CLI hook entries in settings. The hook injects a virtual reminder file during bootstrap but does not modify other skills' configs or persist credentials.
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-robotics - 安装完成后，直接呼叫该 Skill 的名称或使用
/self-improving-robotics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/robotics/`) for coexistence with other skills.
- Added required `Skill: robotics` metadata field and cross-skill precedence/ownership rules.
- Clarified hook arbitration model (single dispatcher, dedupe, rate limiting).
v1.0.0
Initial release for the self-improving-robotics skill:
- Enables structured logging of robotics autonomy failures, incidents, and learnings to markdown files for continuous improvement.
- Captures issues across perception, localization, planning, control, simulation, safety, and hardware integration.
- Provides setup guides for both OpenClaw and generic workspaces, ensuring log directory and files are initialized safely.
- Includes templates and promotion strategies for escalating learnings into safety checklists, calibration playbooks, runbooks, and core project documentation.
- Advises explicit categorization and tagging of issues, learnings, and feature requests for traceable improvement.
元数据
常见问题
Self-Improving Robotics 是什么？
Captures robotics autonomy failures, operational incidents, and engineering learnings to enable continuous improvement across perception, localization, plann... 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 Robotics？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install self-improving-robotics」即可一键安装，无需额外配置。
Self-Improving Robotics 是免费的吗？
是的，Self-Improving Robotics 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Self-Improving Robotics 支持哪些平台？
Self-Improving Robotics 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 Robotics？
由 José I. O.（@jose-compu）开发并维护，当前版本 v1.1.0。
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