AgentSkillsCN

Compare Datasets

对比数据集

SKILL.md

Skill: Compare Datasets

Purpose

Compare metrics, findings, and patterns across two or more connected datasets. Helps identify cross-dataset patterns (e.g., "conversion funnel behavior is similar across both product lines") and dataset-specific anomalies.

When to Use

  • •User says /compare-datasets or "compare across datasets"
  • •After analyzing multiple datasets, to find commonalities
  • •When the user asks "is this pattern unique to this dataset?"

Invocation

/compare-datasets — compare active dataset with all others /compare-datasets {id1} {id2} — compare two specific datasets /compare-datasets metric={name} — compare a specific metric across datasets

Instructions

Step 1: Identify Datasets to Compare

  1. •Read .knowledge/datasets/ to enumerate all connected datasets.
  2. •If specific datasets are named, validate they exist.
  3. •If no datasets specified, use active + all others.
  4. •Require at least 2 datasets. If only 1 exists: "Only one dataset connected. Use /connect-data to add another."

Step 2: Load Metric Dictionaries

For each dataset:

  1. •Read .knowledge/datasets/{id}/metrics/index.yaml
  2. •Build a union of all metric IDs across datasets
  3. •Identify shared metrics (same ID or same name) vs. dataset-specific metrics

Step 3: Compare Shared Metrics

For each metric that exists in 2+ datasets:

  1. •Load the metric YAML from each dataset
  2. •Compare: definition match? (same formula, same unit)
  3. •Compare: typical range overlap? (do the datasets have similar baselines?)
  4. •Compare: guardrails alignment? (are thresholds consistent?)
  5. •Flag discrepancies: "conversion_rate is defined differently in {dataset_a} vs {dataset_b}"

Step 4: Compare Analysis History

For each dataset:

  1. •Read .knowledge/analyses/index.yaml
  2. •Extract key findings from recent analyses
  3. •Look for cross-dataset patterns:
    • •Same finding appearing in multiple datasets
    • •Opposite findings (metric up in one, down in another)
    • •Same root cause identified independently

Step 5: Generate Cross-Dataset Observations

Write findings to .knowledge/global/cross_dataset_observations.yaml:

  • •Shared patterns: behaviors that appear across datasets
  • •Divergences: where datasets behave differently
  • •Metric alignment: which metrics are consistently defined
  • •Suggested investigations: questions raised by the comparison

Step 6: Present Results

Display a comparison table:

code
Cross-Dataset Comparison: {dataset_a} vs {dataset_b}

Shared Metrics: {N} ({M} with matching definitions)
Metric Discrepancies: {list}

Shared Patterns:
  - {pattern description} (seen in both datasets)

Divergences:
  - {metric} is {direction} in {dataset_a} but {direction} in {dataset_b}

Suggested Next:
  - "Investigate why {pattern} differs between datasets"
  - "Align {metric} definitions across datasets"

Edge Cases

  • •Only 1 dataset: Cannot compare — suggest connecting another
  • •No shared metrics: Report this — datasets may serve different purposes
  • •No analysis history: Compare schemas and metric definitions only
  • •Many datasets (>5): Compare pairwise with the active dataset only