Analyze datasets to discover patterns and generate insights
Scanned 6/3/2026
Install to Claude Code
npx -y skills add majiayu000/claude-skill-registry --skill aget-analyze-data-aget-framework-template-analyst-age --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: aget-analyze-data
description: Analyze datasets to discover patterns and generate insights
archetype: analyst
allowed-tools:
- Bash
- Read
- Glob
- Grep
---
# aget-analyze-data
Perform analysis on datasets to discover patterns, trends, and anomalies. Generate actionable insights from data.
## Instructions
When this skill is invoked:
1. **Profile the Dataset**
- Identify data structure (fields, types, record count)
- Assess data quality (completeness, consistency)
- Note any limitations or caveats
2. **Compute Statistics**
- For numerical fields: mean, median, range, distribution
- For categorical fields: frequency counts, top values
- For temporal fields: trends, seasonality
3. **Discover Patterns**
- Identify correlations between fields
- Detect outliers and anomalies
- Find trends over time (if applicable)
4. **Generate Insights**
- Translate findings into actionable statements
- Prioritize by potential impact
- Distinguish correlation from causation
## Output Format
```markdown
## Data Analysis: [Dataset/Topic]
### Dataset Profile
| Attribute | Value |
|-----------|-------|
| Records | [N] |
| Fields | [N] |
| Date Range | [Start - End] |
| Quality | [Good/Fair/Poor] |
### Key Statistics
| Field | Type | Summary |
|-------|------|---------|
| [Field 1] | Numeric | Mean: X, Median: Y, Range: [A-B] |
| [Field 2] | Categorical | Top: [Value] (N%), [Value] (N%) |
### Patterns Discovered
1. **[Pattern Name]**: [Description]
- Evidence: [Data points supporting this]
- Confidence: [High/Medium/Low]
### Anomalies
- [Anomaly 1]: [Description and potential significance]
### Actionable Insights
1. **[Insight]**: [Recommended action based on finding]
### Limitations
- [Limitation 1]: [How it affects conclusions]
```
## Constraints
- **C1**: NEVER present correlation as causation — statistical rigor required
- **C2**: NEVER ignore outliers without explicit justification — anomalies may be valuable
- **C3**: NEVER overfit conclusions to limited data — acknowledge sample limitations
## Related
- SKILL-016: aget-analyze-data specification
- ONTOLOGY_analyst.yaml: Dataset, Analysis, Insight concepts
- CAP-ANL-001: Data Analysis capability
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