Data Catalog Workflow

Goal

Produce a customer-readable Markdown catalog whose YAML frontmatter is a valid DS_CATALOG_V1 machine contract. Preserve uncertainty explicitly so inferred or assumed relationships never appear confirmed.

Flow

1. Confirm the engagement name and the caller-approved durable output path.
2. Inventory entities at business grain. Record source access, tier, volume, profile pointer, classification, lineage, and open questions without copying column-level profile data.
3. Assign every relationship a stable rel-* identifier. Record endpoints, maximum cardinality, both endpoint minimums, one or more paired join-key fields, confidence, and evidence basis.
4. Reconcile coverage counts with the entity and relationship records.
5. Render the human-readable sections from the YAML facts, ending with the canonical Data-Science Coaching disclaimer footer. Narrative can explain facts but cannot redefine them.
6. Validate the artifact with scripts/validate_catalog.py before treating it as ready for review.

Inputs