content-experimentation-best-practices 是做什麼的？
內容實驗與 A/B 測試指南，涵蓋實驗設計、假設、指標、樣本數、統計基礎、CMS 管理的變體以及常見分析陷阱。規劃實驗、設定變體、選擇成功指標、解讀統計結果或建置 CMS 或前端堆疊的實驗工作流程時使用此技能。
內容實驗與 A/B 測試指南，涵蓋實驗設計、假設、指標、樣本數、統計基礎、CMS 管理的變體以及常見分析陷阱。規劃實驗、設定變體、選擇成功指標、解讀統計結果或建置 CMS 或前端堆疊的實驗工作流程時使用此技能。
此技能提供內容實驗與 A/B 測試的全面指引，涵蓋從實驗設計到統計分析的各個階段。對於在內容管理系統（CMS）或前端框架中建立實驗工作流程的開發者與 AI 代理特別實用。它詳細解釋如何制定假設、選擇適當的成功指標（如轉換率或參與度）以及計算所需樣本數。此外，技能還涵蓋了 CMS 管理變體的設定方法，並強調常見的統計分析陷阱（如過早停止實驗或無效的顯著性檢定），幫助使用者避免錯誤的結論。支援的平台包括任何搭配 CMS（如 Sanity）的前端堆疊，並可無縫整合至開發者工作流程中。
npx skills add https://github.com/sanity-io/agent-toolkit/tree/main/skills/content-experimentation-best-practicesInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
內容實驗與 A/B 測試指南，涵蓋實驗設計、假設、指標、樣本數、統計基礎、CMS 管理的變體以及常見分析陷阱。規劃實驗、設定變體、選擇成功指標、解讀統計結果或建置 CMS 或前端堆疊的實驗工作流程時使用此技能。
This repo contains 3 individual skills — each has its own dedicated page.
Structured content modeling guidance for schema design, content architecture, content reuse, references versus embedded objects, separation of concerns, and taxonomies across Sanity and other headless CMSes. Use this skill when designing or refactoring content types, deciding field shapes, debating reusable versus nested content, planning omnichannel content models, or reviewing whether a schema is too page-shaped or presentation-driven.
Sanity development best practices for schema design, GROQ queries, TypeGen, Visual Editing, images, Portable Text, Studio structure, localization, migrations, Sanity Functions, Blueprints, and framework integrations such as Next.js, Nuxt, Astro, Remix, SvelteKit, Angular, Hydrogen, and the App SDK. Use this skill whenever working with Sanity schemas, defineType or defineField, GROQ or defineQuery, content modeling, Presentation or preview setups, Sanity-powered frontend integrations, Sanity Functions, documentEventHandler, defineDocumentFunction, defineMediaLibraryAssetFunction, @sanity/functions, @sanity/blueprints, sanity.blueprint.ts, event-driven content automation, or when reviewing and fixing a Sanity codebase.
SEO and AEO best practices for metadata, Open Graph, sitemaps, robots.txt, hreflang, JSON-LD structured data, EEAT, and content optimized for search engines and AI answer surfaces. Use this skill when implementing page SEO, technical SEO, schema markup, international SEO, AI-overview readiness, or improving content for Google, ChatGPT, Perplexity, and similar assistants.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.