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pmu-data-quality-skillPMU 数据质量技能

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

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周安装

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下载量

1,420
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:pmu-data-quality-skill(PMU 数据质量技能)
来源仓库:https://github.com/clayutk/pmu-data-quality-skill
安装命令:
openclaw skills install pmu-data-quality-skill
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install pmu-data-quality-skill

简介

对 PMU(相量测量单元)数据运行数据质量检查。当用户要求验证、检查或审核 PMU 测量(包括频率、电压)时使用

SKILL.md

name
pmu-data-quality
description
Run data quality checks on PMU (Phasor Measurement Unit) data. Use when the user asks to validate, check, or audit PMU measurements including frequency, voltage magnitude, and phasor angle data against security limits. Also triggers for keywords like "data quality", "PMU check", "out of range", "bad data", or "security limit".
allowed-tools
[Bash, Read, Write, Glob]

PMU Data Quality Checker

Performs automated data quality checks on PMU (Phasor Measurement Unit) CSV data files against configurable security limits defined in IEEE/NERC standards.

What This Skill Checks

  1. Frequency Data — Checks if frequency measurements stay within the nominal range (default: 59.95–60.05 Hz for 60 Hz systems)
  2. Voltage Magnitude Data — Checks if voltage magnitudes stay within acceptable per-unit limits (default: 0.95–1.05 pu)
  3. Phasor Angle Data — Checks if phasor angles stay within expected bounds (default: -180° to +180°, with rate-of-change check)
  4. Missing / NaN Data — Flags rows with missing or null values
  5. Timestamp Continuity — Detects gaps in the reporting rate (e.g., expected 30 samples/sec or 60 samples/sec)

How to Use

Quick check on a CSV file:

python <skill_base_path>/scripts/pmu_quality_check.py path/to/data.csv

With custom limits config:

python <skill_base_path>/scripts/pmu_quality_check.py path/to/data.csv --config <skill_base_path>/templates/limits_config.json

On the template sample data (for testing):

python <skill_base_path>/scripts/pmu_quality_check.py <skill_base_path>/templates/sample_pmu_data.csv

Expected CSV Format

The input CSV should have columns similar to:

timestampfrequencyvoltage_magvoltage_anglecurrent_magcurrent_angle
  • timestamp — ISO 8601 or Unix epoch
  • frequency — in Hz
  • voltage_mag — in per-unit (pu) or kV (specify in config)
  • voltage_angle / current_angle — in degrees
  • current_mag — in per-unit (pu) or Amps

Column names are configurable via the limits config JSON. If the user's CSV uses different column names (like those from openHistorian or FNET exports), update the column_mapping section in the config.

Output

The script produces:

  • A summary report printed to stdout
  • A flagged rows CSV saved alongside the input (e.g., data_flagged.csv)
  • An optional HTML report with charts if --html flag is passed

Workflow

  1. Ask the user for their PMU data file (CSV)
  2. Check if the column names match the expected format; if not, ask or auto-detect
  3. Run the quality check script
  4. Present the summary and ask if the user wants to adjust limits or dig deeper into flagged data

适合场景

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02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

94.01%
按下载量换算1,335

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权限和风险

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安装前确认

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