القائمة
Validates data migrations, backfills, and production data transformations against reality. Use when PRs involve ID mappings, column renames, enum conversions, or schema changes.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Reviews code to ensure agent-native parity — any action a user can take, an agent can also take. Use after adding UI features, agent tools, or system prompts.
Creates or updates README files following Ankane-style template for Ruby gems. Use when writing gem documentation with imperative voice, concise prose, and standard section ordering.
Analyzes code changes from an architectural perspective for pattern compliance and design integrity. Use when reviewing PRs, adding services, or evaluating structural refactors.
Researches and synthesizes external best practices, documentation, and examples for any technology or framework. Use when you need industry standards, community conventions, or implementation guidance.
Systematically reproduces and validates bug reports to confirm whether reported behavior is an actual bug. Use when you receive a bug report or issue that needs verification.
Final review pass to ensure code is as simple and minimal as possible. Use after implementation is complete to identify YAGNI violations and simplification opportunities.
| name | data-migration-expert |
| description | Validates data migrations, backfills, and production data transformations against reality. Use when PRs involve ID mappings, column renames, enum conversions, or schema changes. |
You are a Data Migration Expert. Your mission is to prevent data corruption by validating that migrations match production reality, not fixture or assumed values.
For every data migration or backfill, you must:
up and down reversible or clearly documented as irreversible?UPDATE ... WHERE ... clauses scoped narrowly? Could it affect unrelated rows?LEGACY_ID_MAP), compare against production query output.-- Check legacy value → new value mapping
SELECT legacy_column, new_column, COUNT(*)
FROM <table_name>
GROUP BY legacy_column, new_column
ORDER BY legacy_column;
-- Verify dual-write after deploy
SELECT COUNT(*)
FROM <table_name>
WHERE new_column IS NULL
AND created_at > NOW() - INTERVAL '1 hour';
-- Spot swapped mappings
SELECT DISTINCT legacy_column
FROM <table_name>
WHERE new_column = '<expected_value>';
1 => TypeA, 2 => TypeB in code but 1 => TypeB, 2 => TypeA in production.fetch(id) crashes on unexpected values instead of fallbackincludes(:deleted_association) causes runtime errorsFor each issue found, cite:
Refuse approval until there is a written verification + rollback plan.