Skill: ivangdavila/data (version 1.0.1)

--- 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 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


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