Data
📖 技能介紹
name: Data slug: user-053d27d0-data displayName: Data version: 1.0.1 changelog: Minor refinements for consistency description: Work with data across the full lifecycle from extraction and cleaning to analysis, visualization, and reporting. metadata: {"clawdbot":{"emoji":"📊","requires":{"bins":[]},"os":["linux","darwin","win32"]}}
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
來源於7w4.net。
- 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
- Identify data source (database, file, API)
- Establish connection or load file
- Initial EDA — shape, types, quality issues
- Clean and transform as needed
- Analyze or visualize per user goal
🤖 AI 評測
這個 Skill 質量中等偏上。它最大的優點是覆蓋了資料分析的各個環節，從取數到清洗、分析、視覺化都有涉及，而且特別貼心地列出了各種容易踩的坑，對新手很友好。不足之處在於內容講得比較淺，更像一本目錄手冊而非實戰指南，缺少具體怎麼操作的完整例子。另外，對於現在常用的現代化資料工具基本沒提到，遇到問題也不知道怎麼排查。總的來說，適合打基礎用，但想深入解決複雜問題可能不夠用。
