Data
📖 技能介绍
name: Data slug: 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
这是一份质量中上的数据处理 Skill，结构清晰、内容专业，涵盖了数据分析的各个环节。它的优势在于提供了丰富的实战技巧和多种使用场景的指引，代码示例可直接参考。不足之处是某些内容讲解得不够深入，具体图表生成和复杂数据场景的处理建议较少。总体来说适合入门和日常参考，但进阶用户可能会觉得内容不够用。推荐作为工具书辅助使用，而非深度依赖。
