MCP HubMCP Hub
スキル一覧に戻る

building-cicd-pipelines

jeremylongshore
更新日 Yesterday
78 閲覧
712
74
712
GitHubで表示
メタaitestingautomationdesign

について

このスキルは、GitHub Actions、GitLab CI、Jenkinsなどのプラットフォーム向けにCI/CDパイプライン設定を生成します。ソフトウェア配信におけるビルド、テスト、セキュリティ、デプロイの各ステージを自動化する必要がある場合にご利用ください。一般的なトリガーとベストプラクティスに基づき、プロダクション環境で即時利用可能な設定を作成します。

クイックインストール

Claude Code

推奨
プラグインコマンド推奨
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus
Git クローン代替
git clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/building-cicd-pipelines

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

Overview

This skill empowers Claude to build production-ready CI/CD pipelines, automating software development workflows. It supports multiple platforms and incorporates best practices for testing, building, security, and deployment.

How It Works

  1. Receiving User Request: Claude receives a request for a CI/CD pipeline, including the target platform and desired stages.
  2. Generating Configuration: Claude generates the CI/CD pipeline configuration file (e.g., YAML for GitHub Actions or GitLab CI, Groovy for Jenkins).
  3. Presenting Configuration: Claude presents the generated configuration to the user for review and deployment.

When to Use This Skill

This skill activates when you need to:

  • Create a CI/CD pipeline for a software project.
  • Generate a CI/CD configuration file for GitHub Actions, GitLab CI, or Jenkins.
  • Automate testing, building, security scanning, and deployment processes.

Examples

Example 1: Creating a GitHub Actions Pipeline

User request: "Create a GitHub Actions pipeline with test, build, and deploy stages."

The skill will:

  1. Generate a github-actions.yml file with defined test, build, and deploy stages.
  2. Present the generated YAML configuration to the user.

Example 2: Generating a GitLab CI Configuration

User request: "Generate a GitLab CI configuration that includes security scanning."

The skill will:

  1. Generate a .gitlab-ci.yml file with test, build, security, and deploy stages, including vulnerability scanning.
  2. Present the generated YAML configuration to the user.

Best Practices

  • Security: Integrate static and dynamic analysis tools into the pipeline to identify vulnerabilities early.
  • Testing: Include unit, integration, and end-to-end tests to ensure code quality.
  • Deployment: Use infrastructure-as-code tools to automate infrastructure provisioning and deployment.

Integration

This skill can be used in conjunction with other plugins to automate infrastructure provisioning, security scanning, and deployment processes. For example, it can work with a cloud deployment plugin to automatically deploy applications to AWS, Azure, or GCP after the CI/CD pipeline successfully builds and tests the code.

GitHub リポジトリ

jeremylongshore/claude-code-plugins-plus
パス: backups/skills-batch-20251204-000554/plugins/devops/ci-cd-pipeline-builder/skills/ci-cd-pipeline-builder
aiautomationclaude-codedevopsmarketplacemcp

関連スキル

content-collections

メタ

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

スキルを見る

creating-opencode-plugins

メタ

This skill provides the structure and API specifications for creating OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It offers implementation patterns for JavaScript/TypeScript modules that intercept and extend the AI assistant's lifecycle. Use it when you need to build event-driven plugins for monitoring, custom handling, or extending OpenCode's capabilities.

スキルを見る

evaluating-llms-harness

テスト

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

スキルを見る

sglang

メタ

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

スキルを見る