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在 OpenClaw 中安装
/install outlier-detection-handler
功能描述
Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
安全使用建议
This skill appears coherent and limited to local outlier analysis. Before installing/running: (1) run it in a sandboxed environment or isolated workspace if you will process sensitive data; (2) verify Python 3.10+ and install requirements.txt in a virtualenv to avoid dependency conflicts; (3) validate and restrict input file paths (avoid symlinks or unexpected directories) to prevent accidental data exposure; (4) review the small scripts/main.py if you need absolute assurance (the code is short and readable); (5) for large datasets, test performance and memory use first. No credentials or network access are required by this skill.
功能分析
Type: OpenClaw Skill
Name: outlier-detection-handler
Version: 1.0.1
The skill bundle is a standard implementation for statistical outlier detection using Z-score, IQR, and Grubbs' test. The Python script (scripts/main.py) uses legitimate libraries (numpy, scipy) and follows safe coding practices for data processing, while the SKILL.md provides clear, non-malicious instructions for the AI agent without any signs of prompt injection or unauthorized data access.
能力评估
Purpose & Capability
Name, description, SKILL.md, requirements.txt, and scripts/main.py all describe and implement statistical outlier detection and handling. Declared dependencies (numpy, scipy) and the packaged script are appropriate and proportionate for the stated purpose.
Instruction Scope
SKILL.md instructs validating inputs, running the packaged script, and producing bounded outputs. The instructions reference only workspace files and the packaged script; they do not request unrelated system files, credentials, or external endpoints.
Install Mechanism
There is no install spec; dependencies are standard Python packages listed in requirements.txt and installed via pip as documented. No arbitrary remote downloads, URL shorteners, or archive extraction are used.
Credentials
The skill requires no environment variables, no credentials, and no special config paths. The code reads a user-specified data file or uses built-in demo data — this matches the declared parameters and purpose.
Persistence & Privilege
The skill does not request persistent/always-on presence and does not modify other skills or system-wide settings. It performs local execution only and is user-invocable by default.
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 outlier-detection-handler - 安装完成后，直接呼叫该 Skill 的名称或使用
/outlier-detection-handler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
v1.0.1
Initial release of outlier-detection-handler.
- Provides structured workflows for identifying and managing statistical outliers in data analysis.
- Supports configurable detection methods ("3-sigma", "IQR", "Grubbs") and actions ("flag", "remove", "winsorize").
- Emphasizes explicit assumptions, input validation, reproducible outputs, and clear documentation.
- Includes risk and security checklists, audit-ready commands, and fallback/error-handling guidelines.
- Targets use cases such as data quality control, pre-analysis screening, and regulatory compliance.
v1.0.0
Initial release of outlier-detection-handler.
- Provides a structured workflow for statistical outlier detection and management in data analysis.
- Supports multiple detection methods: 3-sigma, IQR, and Grubbs.
- Offers configurable handling actions: flag, remove, or winsorize outliers.
- Enforces explicit input validation, clear output boundaries, explicit assumptions, and documented fallback paths.
- Includes security, risk, and audit guidance for robust, reproducible execution and regulatory compliance.
元数据
常见问题
Outlier Detection & Handling 是什么？
Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 130 次。
如何安装 Outlier Detection & Handling？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install outlier-detection-handler」即可一键安装，无需额外配置。
Outlier Detection & Handling 是免费的吗？
是的，Outlier Detection & Handling 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Outlier Detection & Handling 支持哪些平台？
Outlier Detection & Handling 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 Outlier Detection & Handling？
由 AIpoch（@aipoch-ai）开发并维护，当前版本 v1.0.1。
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