Document data schemas with headers, fields, types, and evolution rules.
npx skills add https://github.com/qa-aman/claude-skills --skill schema-spec
Please help me install this Agent Skill. Skill: schema-spec Source: https://github.com/qa-aman/claude-skills/tree/main/skills/by-role/data-engineer/schema-spec Command: npx skills add https://github.com/qa-aman/claude-skills --skill schema-spec
Data teams often struggle with undocumented schema elements, inconsistent field definitions, and uncontrolled schema changes that ripple through downstream analytics and data products. This Skill provides a repeatable blueprint to document headers, data types, nullability, keys, and evolution rules to establish a clear data contract between producers and consumers.
Write a new schema spec by specifying the header details and detailing each field's type, nullability, and purpose.
High-intent search queries and answers about installing and using this skill.
A data contract documents schema headers, field types, nullability, and constraints to establish clear boundaries between data producers and consumers. It prevents breaking changes by enforcing evolution rules for adding, removing, or deprecating fields safely.
You document a data dictionary by defining standardized schema headers like Domain, Owner, and Version, then detailing each field's data type, nullability, default values, and business meaning to ensure consistent API payload and table definitions.
The best way to manage schema evolution is specifying explicit evolution policies for adding, removing, renaming, and deprecating fields. This minimizes downstream ripple effects by ensuring backward-compatible schema changes across data platforms.
Yes, this approach applies to relational tables, event schemas, API payloads, and data dictionaries across various data platforms. It captures primary and foreign keys, unique constraints, and indexes to maintain data integrity and enable correct query planning.
Uncontrolled schema changes ripple downstream because inconsistent field definitions and undocumented data types break consumer pipelines. Without a data contract specifying constraints and evolution rules, data products lose integrity and analytics fail.
Yes, you can capture primary and foreign keys, unique constraints, and indexes within the schema spec. Documenting these elements enables correct query planning and ensures data integrity across relational tables and event schemas.
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