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:

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