Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vinvcn/addyosmani-agent-skills-zh --skill ci-cd-and-automationgit clone --depth 1 https://github.com/vinvcn/addyosmani-agent-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/ci-cd-and-automation)<a href="https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/ci-cd-and-automation"><img src="https://agentmods.dev/badge/skills/vinvcn/addyosmani-agent-skills-zh/ci-cd-and-automation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/ci-cd-and-automation"><img src="https://agentmods.dev/badge/skills/vinvcn/addyosmani-agent-skills-zh/ci-cd-and-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.02929 |
| Opus 5 | $0.00023 | $0.01465 |
| Sonnet 5 | $0.00009 | $0.00586 |
| Haiku 4.5 | $0.00005 | $0.00293 |
Grade A, and why
ci-cd-and-automation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI/CD 和自动化
概览
自动化质量门禁,确保任何变更在进入生产环境前都通过测试、lint、类型检查和 build。CI/CD 是其他所有 skill 的执行机制,它能捕捉人类和 agents 漏掉的问题,并且对每一个变更都一致执行。
Shift Left: 尽可能早地在 pipeline 中捕捉问题。Linting 中发现的 bug 只花几分钟;同一个 bug 到生产环境才发现就要花几小时。把检查前移:static analysis 在 tests 之前,tests 在 staging 之前,staging 在 production 之前。
Faster is Safer: 更小批次、更频繁发布会降低风险,而不是增加风险。包含 3 个变更的部署比包含 30 个变更的部署更容易调试。频繁发布会建立对发布流程本身的信心。
何时使用
- 设置新项目的 CI pipeline
- 添加或修改自动化检查
- 配置部署 pipelines
- 当某个变更应触发自动化验证时
- 调试 CI failures
质量门禁 Pipeline
每个变更在合并前都经过这些门禁:
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
任何门禁都不能跳过。 如果 lint 失败,就修 lint,不要禁用规则。如果测试失败,就修代码,不要跳过测试。
GitHub Actions 配置
基础 CI Pipeline
# .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
包含数据库集成测试
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 391 lines · 46 tokens per session scan A 6f1a644b0c32
ci-cd-and-automation is a skill published in the GitHub repository vinvcn/addyosmani-agent-skills-zh (31 stars, last pushed 4mo ago), licensed MIT. It adds 46 tokens to every session and 2,929 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ci-cd-and-automation
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
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.
test-driven-development
Drives development with tests via Red-Green-Refactor and the Prove-It pattern, with hard rules against weakening assertions or faking green suites. Use when implementing any logic, fixing any bug, or changing any behavior. Triggers on "add a feature", "fix this bug", "write tests", or any task where done must be…
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.