Data
📖 技能介绍
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
- 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
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- Analyze or visualize per user goal
🤖 AI 评测
这个 Skill 质量中等偏上。它最大的优点是覆盖了数据分析的各个环节，从取数到清洗、分析、可视化都有涉及，而且特别贴心地列出了各种容易踩的坑，对新手很友好。不足之处在于内容讲得比较浅，更像一本目录手册而非实战指南，缺少具体怎么操作的完整例子。另外，对于现在常用的现代化数据工具基本没提到，遇到问题也不知道怎么排查。总的来说，适合打基础用，但想深入解决复杂问题可能不够用。
