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Data Anomaly Detector
作者
datadrivenconstruction
· GitHub ↗
· v2.1.0
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当前安装
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版本数
在 OpenClaw 中安装
/install data-anomaly-detector
功能描述
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
安全使用建议
Install only if you are comfortable granting filesystem access for construction or business data you choose to analyze. Provide specific datasets, review generated findings before relying on them, and confirm export paths before allowing reports to be written.
功能分析
Type: OpenClaw Skill
Name: data-anomaly-detector
Version: 2.1.0
The skill bundle is designed for data anomaly detection in construction data. The Python code in SKILL.md uses standard data science libraries (pandas, numpy, scipy) for statistical analysis and report generation, without any network calls or shell command execution. The `claw.json` explicitly declares 'filesystem' permission, which is consistent with the `pd.read_excel` usage shown in the quick start guide for loading user-provided data. Neither the markdown instructions nor the code exhibit any signs of malicious intent, such as data exfiltration, persistence mechanisms, or prompt injection attempts to subvert the agent's purpose. All components align with the stated benign functionality.
能力评估
Purpose & Capability
The manifest, instructions, and SKILL.md consistently describe detecting anomalies in construction costs, schedules, productivity, duplicates, sequence gaps, and data quality issues.
Instruction Scope
The instructions mention creating cost estimates and offering exports, which is broader than pure anomaly detection, but it is disclosed in the construction cost-management context and does not add account access, mutation authority, or hidden execution.
Install Mechanism
The bundle contains JSON/Markdown instructions and sample Python code only; there are no install hooks, executables, remote installers, or automatic scripts.
Credentials
Filesystem permission is proportionate for reading user-supplied CSV, Excel, JSON, or direct input and for optional exports, but users should be deliberate about input and output paths.
Persistence & Privilege
No credentials, network endpoints, background workers, durable stores, shell execution, deletion behavior, or privilege escalation are present; the sample history is in-memory only.
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 data-anomaly-detector - 安装完成后，直接呼叫该 Skill 的名称或使用
/data-anomaly-detector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
v2.1.0
- Added detailed documentation and usage examples to SKILL.md for construction anomaly detection.
- Clarified support for statistical (IQR, z-score) and business rule-based anomaly detection for costs and schedules.
- Described construction-specific thresholds and anomaly types handled (cost, schedule, productivity).
- Provided technical overview and sample Python implementation in SKILL.md.
v1.0.0
Data Anomaly Detector 1.0.0 – Initial Release
- Detects anomalies and outliers in construction data, including unusual costs, schedule variances, and productivity spikes.
- Utilizes statistical (IQR, z-score) and business rule-based techniques for anomaly detection.
- Identifies cost overruns, negative costs, and group-specific outliers.
- Flags schedule data issues like negative or excessive task durations.
- Supports automatic anomaly reporting with severity, context, and suggested actions.
元数据
常见问题
Data Anomaly Detector 是什么？
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 4558 次。
如何安装 Data Anomaly Detector？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install data-anomaly-detector」即可一键安装，无需额外配置。
Data Anomaly Detector 是免费的吗？
是的，Data Anomaly Detector 完全免费（开源免费），可自由下载、安装和使用。
Data Anomaly Detector 支持哪些平台？
Data Anomaly Detector 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（darwin, linux, win32）。
谁开发了 Data Anomaly Detector？
由 datadrivenconstruction（@datadrivenconstruction）开发并维护，当前版本 v2.1.0。
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