majiayu000 aget-analyze-data
Perform analysis on datasets to discover patterns, trends, and anomalies. Generate actionable insights from data.
Instructions
When this skill is invoked:
Profile the Dataset
- Identify data structure (fields, types, record count)
- Assess data quality (completeness, consistency)
- Note any limitations or caveats
Compute Statistics
- For numerical fields: mean, median, range, distribution
- For categorical fields: frequency counts, top values
- For temporal fields: trends, seasonality
Discover Patterns
- Identify correlations between fields
- Detect outliers and anomalies
- Find trends over time (if applicable)
Generate Insights
- Translate findings into actionable statements
- Prioritize by potential impact
- Distinguish correlation from causation
Output Format
## 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