mikeparcewski/analysis
skills/data/analysis/SKILL.md
Use when exploring a dataset for patterns, trends, and business insights — EDA, segmentation, anomaly detection, and visualization guidance. Generates the Observation → Insight → Action chain. NOT for schema validation or data quality scoring (use the data/data skill) or SQL queries (use data:analyze).
$ install --global
skillsauthnpx skillsauth add mikeparcewski/wicked-garden analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- analysis
- description:
- |
- NOT for schema validation or data quality scoring (use the data/data skill) or SQL queries (use data:
- analyze).
- phase_relevance:
- ["design", "build"]
- archetype_relevance:
- ["*"]
Data Analysis Skill
Explore datasets, identify patterns, and generate actionable insights.
Quick Start
/wicked-garden:data:analyze explore data/sales.csv
This will:
- Profile the data
- Generate descriptive statistics
- Identify patterns and trends
- Suggest visualizations
- Provide initial insights
After exploration, ask specific questions:
- "What's the trend in sales over time?"
- "Which customer segments are most valuable?"
- "Are there any anomalies in the data?"
Analysis Workflow
1. Profile & Understand
/wicked-garden:data:analyze sales.csv
Key questions:
- What's the grain? (one row per what?)
- What's the date range?
- What are the key metrics?
- Any obvious data quality issues?
2. Explore Patterns
Basic explorations:
- Distributions (histograms, percentiles)
- Categorical breakdowns (frequency tables)
- Time trends (daily/monthly aggregations)
- Correlations (relationship between metrics)
3. Segment Analysis
Common segmentations:
- RFM: Recency, Frequency, Monetary value
- Cohorts: Group by signup/first purchase month
- Geographic: By region, country, city
- Behavioral: By usage patterns
4. Anomaly Detection
Detection methods:
- Statistical outliers (>3 standard deviations)
- Unusual patterns (volume spikes/drops)
- Temporal anomalies (day-of-week deviations)
Insight Generation
Pattern: Observation → Insight → Action
Bad (just observations):
- Average order value is $45
- 60% of customers are in the US
Good (insights with actions):
### Insight: Weekend Shopping Behavior
**Observation**: Sales peak on Friday (+40% vs weekday avg)
**Insight**: Customers shop for weekend needs on Friday.
**Action**: Launch "Friday Flash Sale" campaign (+15% expected)
**Confidence**: HIGH (consistent over 6 months)
Visualization Guidance
| Data Type | Comparison | Best Chart | |-----------|------------|------------| | Time series | Trend over time | Line chart | | Categorical | Compare values | Bar chart | | Distribution | Show spread | Histogram, Box plot | | Relationship | Correlation | Scatter plot | | Composition | Part of whole | Stacked bar, Pie | | Geographic | Location data | Map (choropleth) |
Integration
wicked-garden:data:analyze - Primary tool for data queries via DuckDB:
/wicked-garden:data:analyze data.csv
Native tasks - Document insights via TaskCreate with metadata.event_type="task"
wicked-brain:memory - Store analysis patterns for reuse
Best Practices
- Start simple: Begin with basic aggregations before complex models
- Validate assumptions: Check data quality, verify calculations
- Tell a story: Lead with the "so what?" and make it actionable
- Be honest about uncertainty: Note limitations, quantify confidence
Common Pitfalls
- Correlation ≠ Causation: Ice cream and drownings both increase in summer
- Simpson's Paradox: Aggregate trend may reverse when segmented
- Survivorship Bias: Include failed cases for balanced view
- Cherry-picking: Report all patterns, not just favorable ones
Reference
For detailed content:
- Output Templates - Report and insight templates
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