內容研究指令 - 執行完整的內容研究工作流程。僅在用戶呼叫 cr-full 或要求「content-research」的「full」動作時使用。此為 content-research 技能的進入點。
此技能旨在協助 AI 代理執行完整的內容研究工作流程。使用者只需觸發「cr-full」指令，技能便會自動導向「content-research」技能的「full」動作。它先讀取 _fast.md 與 full.md 這兩個行動文件，簡化標準流程，再將使用者輸入的任何文字視為行動參數。技能遵循快速路徑：以三行規劃任務、平行收集資料、執行腳本，並根據渲染區塊產出報告。若部分階段尚未完成，技能會執行已實作的部分，並標註哪些仍在待辦。檔案位置會因安裝或存放位置而不同，技能會自動偵測並使用正確的路徑。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-fullInstalled? 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 →
執行 content-research 技能的 full 動作。
content-research/actions/_fast.md 以及 content-research/actions/full.md 動作檔案。正常執行時，讀取這些就夠了；只有在某個步驟失敗時才需開啟其他文件。cr-full 之後輸入的任何文字視為該動作的引數。尋找檔案：安裝後，content-research 是此資料夾的同級資料夾 (../content-research/)。在儲存庫中，它位於 ../../ 位置。請使用存在的路徑。
此檔案僅為進入點。請勿在此處複製工作流程邏輯。
This repo contains 3 individual skills — each has its own dedicated page.
Content Research command - write a detailed content brief for a chosen opportunity. Use only when the user invokes cr-brief or asks for the content-research "brief" action. Entry point for the content-research skill.
Content Research command - analyze competing creators and their content. Use only when the user invokes cr-competitors or asks for the content-research "competitors" action. Entry point for the content-research skill.
Content Research command - find high-demand, low-supply content gaps. Use only when the user invokes cr-gaps or asks for the content-research "gaps" action. Entry point for the content-research skill.
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.