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Data Dictionary Builder
Turn raw field descriptions into a complete, ready-to-share data dictionary
What it is
Turn raw field descriptions into a complete, ready-to-share data dictionary
Build a data dictionary entry from a list of table fields. Documents column name, type, description, example values, nullability, foreign key relationships, and business rules. Calibrates depth to the target audience and flags derived or calculated fields explicitly.
What you'll get
Custom instructionsCompiled from your answers, editable before you export.
Data dictionary entry template and reference cardGives Claude a consistent per-field template and a set of common gotchas to document, so every data dictionary entry covers the same required information and flags the things that most commonly confuse new users of the table.
Export bundle for Claude ProjectsCopy-paste blocks with a guided walkthrough.
Try it on
Document the orders tablescenario · built in
Claude should produce a structured entry for each of the five fields. It should mark order_id as PK, user_id as FK with the join target, document all four status enum values with plain-English meanings, note that amount_cents should be divided by 100 for dollar display, and note that created_at is timestamptz (ask about timezone if not stated). It should ask whether any fields are nullable and whether amount_cents is ever zero or negative.
Document the events tablescenario · built in
Claude should document all six fields. It must flag that user_id is nullable and explain the anonymous event use case. It should note that properties is a jsonb payload and that its schema varies by event_name — recommending that a separate per-event schema reference be created. It should document event_timestamp as UTC and warn against joining or filtering without timezone awareness. It should ask whether event_id is guaranteed unique.
Compiled preview
You are a data dictionary writer. Document database tables and fields clearly and completely.
Table or dataset being documented: {{tableSource}}
Documentation audience: {{docAudience}}
Detail level: {{detailLevel}}
Rules:
1. For each field include: column name, data type, description, nullability, and (at {{detailLevel}} detail) business rules and known issues.
2. Flag foreign key relationships, enumerated value sets, and calculated or derived columns explicitly — never describe a derived column as a raw source field.
3. Note any fields whose meaning is ambiguous or context-dependent; ask for clarification rather than guessing.
4. Never invent field names, data types, or business logic not
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