review-docs

作者: apify

審查Apify文件,確保符合風格指南、品質標準與最佳實踐。當使用者說「審查這份文件」、「檢查此頁面」、「稽核…」時使用。

npx skills add https://github.com/apify/apify-docs --skill review-docs

Documentation review

Process

  1. Verify file version - git status to confirm you have the latest

  2. Run deterministic checks (main process) - these are objective, no judgment needed:

    • vale --minAlertLevel=suggestion "<file>" (prose style: voice, tone, terminology, grammar, headings, link text)
    • pnpm lint:md (heading hierarchy, list numbering, spacing)
    • .agents/skills/review-docs/scripts/check-frontmatter.sh "<file>" (description char count)

    Vale carries most of the style guide, so don't re-check its ground by hand. If it isn't installed, say so and note that prose coverage was skipped.

    For a pull request, run all three over every changed .md and .mdx file and report per file.

  3. Delegated review - spawn two subagents in parallel, for what no tool can check. Each reads the page being reviewed plus one standards file, and returns findings with line numbers and suggested fixes:

    • Subagent 1, standards/style-guide.md: bold outside UI elements and critical warnings, link text that doesn't say where it goes, non-parallel lists, missing serial commas, and loose use of "legacy", "alternative", or "deprecated"
    • Subagent 2, standards/page-structure.md: stacked headings, concepts used before they're explained, screenshots that don't earn their place or break the treatment rules, admonitions that don't fit their content, outnumber one per H2 section, or carry content the prose needs
  4. Content review (main process) - focus on what neither tools nor standards cover:

    • Content structure (clear intro, logical flow, next steps)
    • Technical accuracy (code examples correct, API endpoints current)
    • Completeness (prerequisites listed, edge cases covered)
    • Code example quality (complete, runnable, commented where needed)
  5. Format output - per .agents/skills/review-docs/references/review-format.md. Tool output goes in its own section, verbatim with rule names and line numbers. Judgment findings go in a separate section and must not restate anything a tool reported.

For detailed process notes and edge cases, see .agents/skills/review-docs/references/process.md.

來自 apify 的更多技能

apify-influencer-brand-collabs
apify
探索Instagram品牌與創作者的合作關係,透過串聯Apify Actors。當使用者詢問某品牌與誰合作、某創作者曾與哪些品牌進行付費合作時使用…
apify-actor-development
apify
建立、除錯及部署無伺服器雲端程式,用於網頁爬取、自動化及資料處理。支援 JavaScript、TypeScript 及 Python 範本,內建 Crawlee、Playwright 與 Cheerio 函式庫,適用於 HTTP 及瀏覽器爬取。包含透過 apify run 進行本地測試(具備隔離儲存)、輸入/輸出結構驗證,以及透過 apify push 部署至 Apify 平台。需進行 Apify CLI 驗證,並在 .actor/actor.json 中強制加入 generatedBy 元資料以供 AI 使用...
apify-actorization
apify
將現有專案轉換為無伺服器 Apify Actors,並整合語言專屬 SDK。支援 JavaScript/TypeScript(使用 Actor.init() / Actor.exit())、Python(非同步上下文管理器),以及透過 CLI 包裝器的任何語言。提供結構化工作流程:使用 apify init 建立專案骨架、套用 SDK 包裝、設定輸入/輸出架構、以 apify run 進行本地測試,再透過 apify push 部署。包含輸入與輸出架構驗證、Docker 容器化,以及可選的按事件付費...
apify-content-analytics
apify
透過 Apify Actors 進行多平台內容分析,支援 Instagram、Facebook、YouTube 及 TikTok。涵蓋 17 種以上專用 Actors,可處理貼文、Reels、限時動態、留言、Hashtag、粉絲及廣告等內容,並動態使用 mcpc CLI 擷取 Actor 架構,以判斷所需輸入與可用輸出欄位。結果提供三種格式:快速聊天顯示、CSV 匯出或 JSON 匯出,並可自訂結果數量。需在 .env 檔案中設定 Apify Token,並使用 Node.js 20.6+...
apify-ecommerce
apify
從50多個電子商務平台提取產品數據、價格、評論及賣家資訊。三種工作流程模式:產品與定價(價格追蹤、競爭對手分析)、客戶評論(情感分析、品質問題)及賣家情報(透過Google Shopping發現供應商)。支援Amazon(20多個地區)、Walmart、eBay、IKEA、Costco及歐洲零售商;可透過產品網址、分類網址或關鍵字搜尋輸入。可選AI驅動分析,生成價格洞察...
apify-generate-output-schema
apify
為 Apify Actor 分析其原始碼,生成輸出結構(dataset_schema.json、output_schema.json、key_value_store_schema.json)。用於…
apify-influencer-discovery
apify
使用Apify Actors在Instagram、Facebook、YouTube和TikTok上發現並評估網紅。將發現請求路由至15個以上專門的Actors,涵蓋所有主要平台的個人資料抓取、標籤搜尋、互動分析及利基發現。透過mcpc動態獲取Actor架構,以在執行前確定所需輸入與可用輸出欄位。支援三種匯出模式:內嵌聊天顯示、CSV或JSON檔案輸出,並可自訂結果數量...
apify-ultimate-scraper
apify
自動化網頁爬蟲,為55多個平台選擇最佳Actor,包括Instagram、TikTok、YouTube、Facebook、Google地圖等。涵蓋8大主要平台的55多個預配置Actor,並提供針對特定使用案例的選擇指引(潛在客戶開發、網紅發現、品牌監控、競爭對手分析、趨勢研究)。支援三種輸出格式:快速聊天顯示、CSV匯出或JSON匯出,並可自訂結果數量限制。包含多Actor工作流程模式,適用於複雜...