Data

分类：来源待确认 ｜ 文件数：12 ｜ 仓库目录：data

📌 简介

Work with data across the full lifecycle from extraction and

🎯 适用场景

适用于该技能的能力范围，详见下方「📖 使用说明」。

📂 目录结构

  - .gitignore
  - LICENSE
  - README.md
  - SKILL.md
  - _meta.json
  - _skillhub_meta.json
  - analysis.md
  - cleaning.md
  - patterns.md
  - quality.md
  - querying.md
  - visualization.md

🚀 安装方法

将本文件夹整体复制到 WorkBuddy 的技能目录即可启用：

# 用户级（推荐）
cp -r . ~/.workbuddy/skills/data

# 或项目级
cp -r . <你的项目>/.workbuddy/skills/data

复制完成后，重启或刷新 WorkBuddy，即可在对话中用自然语言触发该技能。

⚙️ 配置说明

本技能开箱即用，无需额外配置。若涉及外部 API 调用，请在使用时按需提供您自己的密钥（不要提交到公开仓库）。

📖 使用说明（完整规范）

以下为该技能的完整说明，涵盖核心能力、工作流程与关键规则，帮助您全面了解其运作方式。

When to Use

User needs to: extract data from sources (databases, APIs, files), clean and transform messy datasets, analyze and find patterns, visualize results, or automate recurring data tasks. Agent handles the full data workflow.

Quick Reference

Area	File	Focus
Querying & Extraction	querying.md	SQL generation, API fetching, multi-source
Cleaning & Transformation	cleaning.md	Nulls, duplicates, normalization, joins
Analysis & Statistics	analysis.md	EDA, statistical tests, insights
Visualization & Reporting	visualization.md	Charts, dashboards, exports
Quality & Validation	quality.md	Data checks, anomaly detection, drift
Workflow Patterns	patterns.md	Common data workflows, automation

Core Operations

Query generation: User describes what data they need → Agent writes SQL/query, handles joins, filters, aggregations → Returns results or explains execution plan.

Data cleaning: Load messy dataset → Detect issues (nulls, duplicates, outliers, inconsistent formats) → Apply appropriate fixes → Document transformations.

Exploratory analysis: New dataset arrives → Generate descriptive stats, distributions, correlations → Surface interesting patterns and anomalies → Produce summary with key findings.

Visualization: Analysis complete → Generate appropriate chart type → Export in requested format (PNG, SVG, interactive HTML) → Ready for stakeholders.

Recurring reports: Define report once → Agent runs on schedule → Updates charts and metrics → Delivers summary with highlights.

Critical Rules

• Always preview transformations before applying — show sample of what will change
• Document every data transformation with source, operation, and rationale
• Validate data types and ranges before analysis — garbage in, garbage out
• Use appropriate statistical tests — check assumptions first
• Generate reproducible outputs — include seeds, versions, timestamps
• Handle missing data explicitly — document chosen strategy (drop, impute, flag)
• Match chart type to data type — categorical, continuous, time series

User Modes

Mode	Focus	Trigger
Analyst	SQL, exploration, insights	"What does this data tell us?"
Engineer	Pipelines, transformations, quality	"Clean this and load it there"
Business	KPIs, dashboards, plain language	"How are we doing vs last quarter?"
Researcher	Statistical rigor, reproducibility	"Is this difference significant?"
Developer	Schema design, API data, types	"Generate types from this JSON"

See patterns.md for workflows per mode.

On First Use

1. Identify data source (database, file, API)
2. Establish connection or load file
3. Initial EDA — shape, types, quality issues
4. Clean and transform as needed
5. Analyze or visualize per user goal

⚠️ 注意事项

• 本技能从本地 WorkBuddy 环境导出，所有真实密钥 / 凭据 / 个人数据均已脱敏为占位符，重新使用前请配置您自己的 Key。
• 如为原创技能，可自由使用、修改与再分发；若对外分享请保留作者与来源信息。
• 技能提供的是自动化辅助能力，不替代专业判断；涉及交易、法律、医疗等高风险场景请谨慎并自担风险。

📄 许可证

MIT License —— 详见仓库内 LICENSE 文件。