基于功能设计文档与现有源码，端到端实现一个产品功能：开发、代码 review、测试用例编写与执行， 循环迭代直到测试通过，最终产出用户手册与实现总结文档（保存到当前项目 markdown/ 目录）。 触发条件：用户提供了功能设计文档（PRD/设计文档/需求描述）+ 源码目录（或代码库路径）， 并希望"实现这个功能"、"开发这个功能"、"把这个设计文档变成代码"、"做这个功能的开发+测试"、 "完整实现并测试"、"端到端开发"，或明确要求按"开发-review-测试"循环推进直到测试通过。 也适用于需要额外运行环境的开发任务，例如 PostgreSQL/MySQL 等数据库插件开发 （可能需要编译、搭建实例、配置参数、初始化、跑测试）、需要启动服务进程才能测试的后端功能、 需要特定运行时环境的库或框架功能。 即使用户只是说"帮我把这个功能做出来"、"按设计文档实现一下"、"这个功能开发完了麻烦测试一下"， 但同时给出了设计文档和代码路径，也应使用本 skill。 本 skill 的核心是严格的"开发 ⇄ Review"循环 + "测试者不预先看代码"的盲测试原则， 因此当用户提到"独立测试"、"测试不能看代码"、"先写测试用例再看代码"、"black-box 测试"时， 也应优先使用本 skill。 不适用于：只是要求"写一个脚本"、"改一行代码"、"快速修一个 bug"等轻量级、 无需走完整开发-review-测试闭环的任务；也不适用于纯粹的代码评审（无需新开发功能） 或纯粹的测试用例编写（无设计文档、无需开发）这类单一环节请求—— 这些更适合直接处理而非套用本 skill 的完整闭环流程。

Write Chinese Markdown articles for university students and adults with social experience that explain one viewpoint, axiom, theorem, law, principle, or theory system through "求真讲法、求存讲法、思考", with text/Mermaid and standalone GitHub-renderable SVG files, assumptions, derivation or motivation, applicability boundaries, positive and negative examples, and save the result under the current project's markdown directory. Use when Codex is asked to generate 公理讲解文案、定理讲解、理论体系通俗解读、观点拆解、面向大学生和成年人的图文并茂 Markdown 文章.

Triage open pull requests for CI status, review comments, and merge readiness. Use in PR babysitter loops. Respects project review norms and required checks.

Parse CI failures, identify failing job/step, classify as flake, regression, env, or config. Use in CI sweeper loops before any fix attempt.

Takes a structured list of changes from changelog-scan and produces a clean, categorized, user-facing release notes draft. Follows project voice and conventions. Never publishes — only proposes a draft file or section.

Scan recent merges to main (and noteworthy direct commits) since a given window (last tag or date in state). Extract titles, labels, types, linked issues, and signals for breaking changes or security. Produces structured input for a release notes drafter. Use in changelog-drafter loops.

Add a new operation to the OpenVINO GPU plugin — OpenCL kernel design, oneDNN-backed paths, sub-group/LWS tuning, and functional tests.

Detect and fix clang-format, clang-tidy, and copyright header violations in an OpenVINO C++ codebase. Use when the user complains about code style or formatting, asks to clean up changes, fix linting, add a copyright header, or when a style check or linting CI job is failing. Do not use for build errors, compilation failures, linker errors, test failures, runtime crashes, accuracy issues, or CMake config problems.

Cut a stable release of Paseo (fresh patch or minor, or promote from beta). Use when the user says "release stable", "ship stable", "promote", "release:patch", "release:minor", "release:promote", or "/release-stable".

Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", "single URL", "check this page", "page analysis", or provides a single URL for review.

Full-site crawling, scraping, and site mapping via Firecrawl MCP. Use when user says "crawl site", "map site", "full crawl", "find all pages", "broken links", "site structure", "discover pages", "JS rendering", or needs site-wide analysis.

AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".

Use when a focused Sales workflow needs Salesforce-backed CRM reads, links, drafts, notes, account plans, Agentforce assignments, or explicitly requested Salesforce connector/query guidance. Do not use for generic CRM app construction or when another CRM is authoritative.

Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions.

Create, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable.

Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.

Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.

Use this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM auditing.

Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. Don't use for database administration or replication strategies outside volume provisioning context.

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).