Data Fitness Review

Analyze the codebase (or specified files/modules) for data fitness. Identify gaps in schema design, migration safety, data integrity, query correctness, data modeling, and pipeline quality using evidence from the code, schema definitions, migration files, and configuration.

Reference: Fundamentals of Databases, Fundamentals of Data Engineering, Fundamentals of Graph Databases — see also Fundamentals

Domain Knowledge

• references/rubric.md is the scoring source: severity definitions, score anchors per dimension, the N/A rule, scope boundaries, and the overall-score formula. Read it first; it is short.
• references/checklist.md lists what to inspect in each dimension during steps 4-9.
• references/wisdom.md is optional background, auto-generated from the three articles above. It has no rubric. When you need depth on one topic (normalization, CDC, data quality dimensions), grep it for that heading and read only that section; never read it whole (~25k tokens).

Configuration

Invoke the resolver CLI to obtain effective weights and thresholds for the review target. Never load fitness-config.json directly.

python3 "${CLAUDE_SKILL_DIR}/../../scripts/fitness-config.py" show --path <target>