AgentSkillsCN

aget-analyze-data

分析数据集以发现模式并生成洞见

SKILL.md
--- frontmatter
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