data-analysis

name	data-analysis
description	Experiment result analysis with reproducible metrics, charts, and Korean decision summaries. Triggers: analyze results, compare metrics, visualize data, experiment analysis.

data-analysis

Usage

Use this skill when comparing runs, interpreting metrics, or producing analysis-ready artifacts from result files.

Responsibility

This skill owns analysis workflow only:

• Scope: data loading, metric comparison, visualization, and report-ready summaries.
• Out of scope: model retraining, production deployment, or architecture refactor.

Path Policy

• temp/: temporary merged datasets and scratch notebooks/scripts
• documents/drafts/: interim analysis narrative and open questions
• documents/final/: final Korean analysis report and decision summary
• documents/reference/: reusable analysis methodology and metric definitions

Protocol

1. Define analysis question

• Clarify baseline, success metrics, and comparison window.

2. Collect and normalize inputs

• Load results from canonical result/log sources.
• Normalize schema and units before comparison.

3. Compute metrics

• Calculate primary and secondary metrics consistently.
• Include uncertainty or variance when available.

4. Visualize

• Use readable plots with clear labels and units.
• Avoid decorative charts that hide magnitude.

5. Interpret

• Distinguish observation from recommendation.
• Explain practical impact in Korean for documents/ outputs.

6. Publish artifacts

• Keep temporary calculation files in temp/.
• Move narrative into documents/drafts/, then finalize in documents/final/.

Output Contract

Each delivery contains:

• Data scope and baseline
• Metric definitions used
• Key plots or tables
• Main findings and limits
• Recommended next experiment or action

Quality Bar

• Reproducible metric calculations
• Explicit assumptions and filtering rules
• No conclusion without numeric evidence