/cicd-integration

name: cicd-integration
description: "Generate CI/CD pipeline configurations that automate design system quality checks — token validation, component linting, visual regression, accessibility scanning, and release gating. Produces ready-to-use pipeline files for GitHub Actions, GitLab CI, CircleCI, or Bitbucket Pipelines, configured to enforce the standards that audit skills check manually. Trigger when someone says: set up CI for the design system, automate these checks, add pipeline for tokens, create GitHub Action for design system, CI/CD for components, automate the release process, continuous integration for design system, how do I automate what the audit found, quality gates in CI, or anything about automating design system quality checks in a pipeline. Do NOT trigger for running a manual audit — use the specific audit skill for that. Do NOT trigger for generating a release checklist — use change-communication for that."
references:
  - ../../knowledge-notes/component-governance.md
  - ../../knowledge-notes/token-architecture.md
  - ../../knowledge-notes/design-to-code-contract.md

CI/CD Integration

A skill for generating pipeline configurations that automate the quality checks Design System Ops skills perform manually. This bridges the gap between "here are the problems the audit found" and "these problems can never recur because the pipeline catches them."

**Output type:** File creation. This skill produces pipeline configuration files (YAML), scripts, and documentation. It does not execute pipelines — it generates the configuration that teams add to their repository.

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Why this exists

Every audit skill in Design System Ops finds problems. Some of those problems should never have reached a human reviewer because they are mechanically detectable: a hardcoded hex value in a component file, a token alias that references a non-existent token, a component export missing from the barrel file, an accessibility violation that an automated scanner would catch.

CI/CD Integration converts audit findings into automated pipeline checks. The goal is not to replace the audit skills — those handle nuance, context, and cross-skill synthesis that pipelines cannot. The goal is to automate the mechanical subset so that audits focus on the problems only humans can evaluate.

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Configuration

Check for `.ds-ops-config.yml` in the project root:

cicd:
  platform: "github-actions"         # github-actions, gitlab-ci, circleci, bitbucket
  package_manager: "npm"              # npm, yarn, pnpm
  node_version: "20"                  # Node.js version
  test_framework: "jest"              # jest, vitest, playwright
  component_library_path: "packages/components"
  token_path: "packages/tokens"
  monorepo: true                      # Whether the project uses a monorepo structure
  triggers:
    - "push to main"
    - "pull request to main"

If no configuration exists, ask for: 1. CI/CD platform (default: GitHub Actions) 2. Package manager (default: npm) 3. Whether the project is a monorepo 4. Paths to token files and component files

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Step 0: Assess what to automate

Before generating any pipeline configuration, determine which checks are worth automating. Not everything should be in CI.

Automation decision matrix

| Check type | Automate in CI? | Why | |---|---|---| | Token naming violations | Yes | Mechanical — regex patterns, no judgment needed | | Token circular references | Yes | Graph traversal — computers are better at this | | Hardcoded colour values | Yes | grep/AST — exact match detection | | Component prop type checking | Yes | TypeScript/Flow already does this | | Accessibility (automated subset) | Yes | axe-core catches 30–40% of WCAG violations | | Visual regression | Yes | Pixel comparison catches unintended changes | | Component export completeness | Yes | AST/barrel file check | | Bundle size tracking | Yes | Byte comparison — pure measurement | | Token coverage gaps | Partial | Can check primitive→semantic mapping exists, cannot judge if mappings are correct | | Component API consistency | Partial | Can lint prop naming patterns, cannot judge API design quality | | Documentation completeness | Partial | Can check if docs exist, cannot judge if they are good | | Cross-component pattern compliance | No | Requires too much context and judgment | | Naming convention quality | No | Conventions need human validation first, then automation | | Usage guideline adherence | No | Requires consuming-app context that CI rarely has |

Rule of thumb

If the skill's finding includes a specific, unambiguous rule (e.g., "tokens must use kebab-case", "no hex values outside token files"), it can be automated. If the finding requires judgment (e.g., "this token naming could be clearer"), it cannot.

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Step 1: Map audit findings to pipeline checks

For each audit finding category, determine the automated check:

Token checks

| Finding category | Pipeline check | Tool | |---|---|---| | Naming violations | Lint token names against convention regex | Custom script or Style Dictionary validator | | Circular references | Build-time alias resolution check | Style Dictionary build (fails on circular refs) | | Orphaned tokens | Cross-reference token definitions with usage in component files | Custom script: grep token names across component source | | Missing semantic tier | Check that every component token reference resolves through a semantic alias | Custom script or Style Dictionary referencing | | DTCG format compliance | Validate token files against DTCG schema | JSON Schema validation |

Component checks

| Finding category | Pipeline check | Tool | |---|---|---| | Export completeness | Verify barrel file exports match component directories | Custom script: compare fs listing with exports | | Prop type safety | TypeScript strict mode compilation | `tsc --noEmit` | | Accessibility | Run axe-core on rendered components | `@axe-core/cli`, `jest-axe`,