gitlab-coding-principles

Testing & Quality

MUST USE before planning, implementing, refactoring, OR reviewing any GitLab code changes (including merge request reviews, code review feedback, new features, bug fixes). Evaluate every principle group to ensure cross-domain coverage. Triggers: review, reviewing, plan, planning, implement, implementing, refactor, refactoring, MR review, code review.

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Source SKILL.md: https://github.com/gitlabhq/gitlabhq/blob/HEAD/.claude/skills/gitlab-coding-principles/SKILL.md

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Load Project Principles

Evaluate ALL groups below and load principles from EVERY group that applies to your task. Most tasks span multiple groups (e.g., a model change may need Backend, Database, and Testing principles). DO NOT stop after the first group. When your task involves database queries, scopes, or data access patterns, ALWAYS load Database principles regardless of which files you are editing.

Database:

Database fundamentals (model queries, batching, sharding, partitioning, N+1): Read .ai/principles/distilled/database-fundamentals.md (load for any database work)
Database migration patterns and zero-downtime safety: Read .ai/principles/distilled/database-migrations.md (also load: .ai/principles/distilled/database-fundamentals.md)
Column types, constraints, indexes, naming conventions: Read .ai/principles/distilled/database-schema.md (also load: .ai/principles/distilled/database-fundamentals.md)
SQL performance, transactions, batching: Read .ai/principles/distilled/database-queries.md (also load: .ai/principles/distilled/database-fundamentals.md)
ClickHouse and Siphon CDC concerns, including adding the matching column to the respective siphon_ ClickHouse table when adding a column to a Postgres table replicated by Siphon: Read .ai/principles/distilled/clickhouse.md (also load: .ai/principles/distilled/database-fundamentals.md)

Cells:

Cells architecture sharding keys, organization data ownership, routable requests, and cell isolation when adding tables or customer-owned resources: Read .ai/principles/distilled/cells-fundamentals.md (load for any cells work)
Cells globally-unique attribute claims (Cells::Claimable), claim rollout lifecycle, and feature flags for cross-cell uniqueness: Read .ai/principles/distilled/cells-claims.md (also load: .ai/principles/distilled/cells-fundamentals.md)

Security:

Security vulnerabilities and secure coding: Read .ai/principles/distilled/security.md
Authentication, authorization, token handling, OAuth, SAML, identity linking, composite identity, session management, 2FA, MFA, password management: Read .ai/principles/distilled/authentication.md

Permissions:

Where to check permissions, naming conventions, role definitions: Read .ai/principles/distilled/permissions-fundamentals.md (load for any permissions work)
Granular PAT (GPAT) compliance for GraphQL types and mutations: Read .ai/principles/distilled/permissions-graphql-gpat.md (also load: .ai/principles/distilled/permissions-fundamentals.md)
Granular PAT (GPAT) compliance for REST endpoints and job tokens: Read .ai/principles/distilled/permissions-rest-gpat.md (also load: .ai/principles/distilled/permissions-fundamentals.md)

Code Review:

General code review practices and acceptance checklist: Read .ai/principles/distilled/code-review.md
Reviewing AI instruction files under .ai/ (hand-authored modules, distilled principles, baselines, distillation config): Read .ai/principles/distilled/ai-instruction-files.md

Backend:

Ruby/Rails style, logging, common pitfalls: Read .ai/principles/distilled/backend-ruby.md (load for any backend work)
Service patterns, abstractions, design conventions: Read .ai/principles/distilled/backend-architecture.md (also load: .ai/principles/distilled/backend-ruby.md)
EE/CE separation, licensing, code placement: Read .ai/principles/distilled/backend-ee.md (also load: .ai/principles/distilled/backend-ruby.md)

API:

REST API design and conventions: Read .ai/principles/distilled/rest-api.md
GraphQL API design and conventions: Read .ai/principles/distilled/graphql.md

Workers:

Sidekiq worker design and reliability: Read .ai/principles/distilled/workers.md

CI/CD:

GitLab CI/CD internals — adding pipeline configuration keywords (Ci::Config::Entry classes, feature-flag gating, JSON schema) and creating CI routing/partitioned tables. NOTE this is about GitLab's own CI implementation, not authoring a project's .gitlab-ci.yml: Read .ai/principles/distilled/cicd-internals.md

Frontend:

Vue.js components, state management, patterns: Read .ai/principles/distilled/frontend-vue.md (load for any frontend work)
CSS/SCSS, Tailwind, dark mode: Read .ai/principles/distilled/frontend-style.md (also load: .ai/principles/distilled/frontend-vue.md)
HAML templates, ViewComponents, Pajamas: Read .ai/principles/distilled/frontend-haml.md (also load: .ai/principles/distilled/frontend-vue.md)
Frontend accessibility patterns and requirements: Read .ai/principles/distilled/frontend-a11y.md (also load: .ai/principles/distilled/frontend-vue.md)

Testing:

RSpec patterns, factories, shared examples: Read .ai/principles/distilled/testing-rspec.md
Test quality for authentication and authorization specs — positive and negative path coverage, token edge cases (expired, revoked, wrong-scope), the authorization actor matrix (authorized, unauthorized, anonymous, admin), auth-stubbing red flags, rate-limit specs, and narrow token scopes in specs: Read .ai/principles/distilled/authentication-testing.md
Jest, jsdom, Vue Test Utils patterns: Read .ai/principles/distilled/testing-jest.md
Rails migrations testing patterns: Read .ai/principles/distilled/testing-migrations.md
Frontend testing hierarchy — which test type to write (unit vs MSW integration vs Capybara vs QA), lazy-load discovery, and cross-layer anti-patterns: Read .ai/principles/distilled/testing-frontend-testing-hierarchy.md
How to write MSW integration tests correctly (setup, mounting, handlers, fixtures, request counting): Read .ai/principles/distilled/testing-msw.md
End-to-end (QA) test patterns — resource readiness and explicit waits, reliable navigation, and avoiding timing-based flakiness in qa/ specs: Read .ai/principles/distilled/testing-e2e.md

Performance:

Performance and scalability: Read .ai/principles/distilled/performance.md

Documentation:

Documentation style and completeness: Read .ai/principles/distilled/documentation.md
Documentation authoring guidelines per topic type (for example, Concept, Task, Reference, Troubleshooting, Tutorial, etc.): Read .ai/principles/distilled/documentation-topics.md
Documenting REST, GraphQL, and CLI APIs (endpoint reference structure, attributes tables, curl examples): Read .ai/principles/distilled/documentation-api.md
Documenting feature state and lifecycle (feature flags, experiment/beta status, version history, release notes): Read .ai/principles/distilled/documentation-feature-lifecycle.md

Feature Flags:

Feature flag usage and lifecycle: Read .ai/principles/distilled/feature-flags.md

Analytics:

Analytics instrumentation and metrics: Read .ai/principles/distilled/analytics.md
Git, commits, or branches: Read .ai/git.md
CI/CD pipelines or .gitlab-ci.yml: Read .ai/ci-cd.md