mikeparcewski/data
skills/data/data/SKILL.md
Use when profiling a dataset's structure, validating it against a schema, or generating a data quality report (completeness, uniqueness, validity constraints). Runs the data_profiler.py and schema_validator.py scripts. NOT for exploratory pattern analysis (use data/analysis) or SQL queries (use data:analyze).
$ install --global
skillsauthnpx skillsauth add mikeparcewski/wicked-garden dataInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- data
- description:
- |
- NOT for exploratory pattern analysis (use data/analysis) or SQL queries (use data:
- analyze).
- phase_relevance:
- ["design", "build"]
- archetype_relevance:
- ["*"]
Data Engineering Skill
Core data engineering operations for profiling, validation, and quality assessment.
Quick Start
Profile a Dataset
/wicked-garden:data:data profile path/to/data.csv
This will:
- Detect file format
- Sample rows (head/random/tail)
- Infer schema and types
- Calculate quality metrics
- Generate recommendations
Validate Schema
/wicked-garden:data:data validate --schema schema.json --data data.csv
Checks: Column presence, type conformance, constraint validation, nullability rules.
Assess Quality
/wicked-garden:data:data quality data.csv
Reports on: Completeness (null rates), Uniqueness (duplicates), Validity (constraints), Consistency (cross-field checks).
Commands
| Command | Purpose |
|---------|---------|
| /wicked-garden:data:data profile <path> | Profile dataset structure and quality |
| /wicked-garden:data:data validate | Validate data against schema |
| /wicked-garden:data:data quality <path> | Generate quality report |
Dataset Profiling
Uses data_profiler.py script:
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/scripts/data/data_profiler.py" \
--input data.csv --output profile.json
Output includes:
- Row count and column count
- Column types (inferred)
- Null rates per column
- Cardinality (distinct values)
- Sample values
- Statistical summaries for numeric columns
Schema Validation
Uses schema_validator.py script. Define expected columns with:
- name: Column name
- type: integer, string, decimal, datetime, date
- nullable: true/false
- constraints: unique, min, max, pattern, enum
See examples for schema format.
Quality Dimensions
| Dimension | Metric | Threshold | |-----------|--------|-----------| | Completeness | Null rate | <5% | | Uniqueness | Duplicate rate | <1% | | Validity | Type conformance | 100% | | Consistency | Cross-field rules | 100% |
Integration
| Plugin | Enhancement |
|--------|-------------|
| wicked-garden:data:analyze | Use for SQL-based profiling of large files via DuckDB |
| Native tasks | Document quality issues via TaskCreate with metadata.event_type="task" |
| wicked-brain:memory | Store quality patterns across sessions |
Large Files
For files >1GB, use wicked-garden:data:analyze for efficient SQL-based profiling:
/wicked-garden:data:analyze large_file.csv
Output Standards
All reports include:
- Summary: High-level findings
- Metrics: Quantitative measurements
- Issues: Prioritized problems
- Recommendations: Actionable next steps
- Confidence: Assessment reliability
Reference
For detailed examples and patterns:
- Examples - Profile output, schema format, quality report
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