Atlas / Skills / addyosmani / Ci Cd And Automation

Ci Cd And AutomationSAFE

skills/addyosmani/ci-cd-and-automation

Production-grade engineering skills for AI coding agents.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
99,537
01

Overview

Production-grade engineering skills for AI coding agents.

Read from source at commit 9be8f7674e77OBSERVED · 2026-09-28
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: ci-cd-and-automation
description: Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
---

# CI/CD and Automation

## Overview

Automate quality gates so that no change reaches production without passing tests, lint, type checking, and build. CI/CD is the enforcement mechanism for every other skill — it catches what humans and agents miss, and it does so consistently on every single change.

**Shift Left:** Catch problems as early in the pipeline as possible. A bug caught in linting costs minutes; the same bug caught in production costs hours. Move checks upstream — static analysis before tests, tests before staging, staging before production.

**Faster is Safer:** Smaller batches and more frequent releases reduce risk, not increase it. A deployment with 3 changes is easier to debug than one with 30. Frequent releases build confidence in the release process itself.

## When to Use

- Setting up a new project's CI pipeline
- Adding or modifying automated checks
- Configuring deployment pipelines
- When a change should trigger automated verification
- Debugging CI failures

## The Quality Gate Pipeline

Every change goes through these gates before merge:

```
Pull Request Opened
    │
    ▼
┌─────────────────┐
│   LINT CHECK     │  eslint, prettier
│   ↓ pass         │
│   TYPE CHECK     │  tsc --noEmit
│   ↓ pass         │
│   UNIT TESTS     │  jest/vitest
│   ↓ pass         │
│   BUILD          │  npm run build
│   ↓ pass         │
│   INTEGRATION    │  API/DB tests
│   ↓ pass         │
│   E2E (optional) │  Playwright/Cypress
│   ↓ pass         │
│   SECURITY AUDIT │  npm audit
│   ↓ pass         │
│   BUNDLE SIZE    │  bundlesize check
└─────────────────┘
    │
    ▼
  Ready for review
```

**No gate can be skipped.** If lint fails, fix lint — don't disable the rule. If a test fails, fix the code — don't skip the test.

## GitHub Actions Configuration

### Basic CI Pipeline

```yaml
# .github/workflows/ci.yml
name: CI

on:
  pull_request:
    branches: [main]
  push:
    branches: [main]

jobs:
  quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-node@v4
        with:
          node-version: '22'
          cache: 'npm'

      - name: Install dependencies
        run: npm ci

      - name: Lint
        run: npm run lint

      - name: Type check
        run: npx tsc --noEmit

      - name: Test
        run: npm test -- --coverage

      - name: Build
        run: npm run build

      - name: Security audit
        run: npm audit --audit-level=high
```

### With Database Integration Tests

```yaml
  integration:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:16
        env:
          POSTGRES_DB: testdb
          POSTGRES_USER: ci_user
          POSTGRES_PASSWORD: ${{ secrets.CI_DB_PASSWORD }}
        ports:
          - 5432:5432
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5

    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '22'
          cache: 'npm'
      - run: npm ci
      - name: Run migrations
        run: npx prisma migrate deploy
        env:
          DATABASE_URL: postgresql://ci_user:${{ secrets.CI_DB_PASSWORD }}@localhost:5432/testdb
      - name: Integration tests
        run: npm run test:integration
        env:
          DATABASE_URL: postgresql://ci_user:${{ secrets.CI_DB_PASSWORD }}@localhost:5432/testdb
```

> **Note:** Even for CI-only test databases, use GitHub Secrets for credentials rather than hardcoding values. This builds good habits and prevents accidental reuse of test credentials in other contexts.

### E2E Tests

```yaml
  e2e:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '22'
          cache: 'npm'
      - run: npm ci
      - name: Install Playwright
        run: npx playwright install --with-deps chromium
      - name: Build
        run: npm run build
      - name: Run E2E tests
        run: npx playwright test
      - uses: actions/upload-artifact@v4
        if: failure()
        with:
          name: playwright-report
          path: playwright-report/
```

## Feeding CI Failures Back to Agents

The power of CI with AI agents is the feedback loop. When CI fails:

```
CI fails
    │
    ▼
Copy the failure output
    │
    ▼
Feed it to the agent:
"The CI pipeline failed with this error:
[paste specific error]
Fix the issue and verify locally before pushing again."
    │
    ▼
Agent fixes → pushes → CI runs again
```

**Key patterns:**

```
Lint failure → Agent runs `npm run lint --fix` and commits
Type error  → Agent reads the error location and fixes the type
Test failure → Agent follows debugging-and-error-recovery skill
Build error → Agent checks config and dependencies
```

## Deployment Strategies

### Preview Deployments

Every PR gets a preview deployment for manual testing:

```yaml
# Deploy preview on PR (Vercel/Netlify/etc.)
deploy-preview:
  runs-on: ubuntu-latest
  if: github.event_name == 'pull_request'
  steps:
    - uses: actions/checkout@v4
    - name: Deploy preview
      run: npx vercel --token=${{ secrets.VERCEL_TOKEN }}
```

### Feature Flags

Feature flags decouple deployment from release. Deploy incomplete or risky features behind flags so you can:

- **Ship code without enabling it.** Merge to main early, enable when ready.
- **Roll back without redeploying.** Disable the flag instead of reverting code.
- **Canary new features.** Enable for 1% of users, then 10%, then 100%.
- **Run A/B tests.** Compare behavior with and without the feature.

```typescript
// Simple feature flag pattern
if (featureFlags.isEnabled('n
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (1)

MEDIUMInventory / provenance · inv.symlink · CWE-1104
.opencode/skills
.opencode/skills
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-09-28 · audit v0.4.1 · source sha 9be8f7674e77full audit observations/trust-audit/skill/addyosmani__ci-cd-and-automation.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-09-289be8f7674e77SAFEB89first audit
05

Questions

What does the Ci Cd And Automation skill do?

Production-grade engineering skills for AI coding agents.

Is Ci Cd And Automation safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Ci Cd And Automation access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (9be8f7674e77), read on 2026-09-28. The repository is watched, and a new audit runs when it changes — this is the first audit.

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