內容研究指令 - 找出高需求、低供應的內容缺口。僅在使用者呼叫 cr-gaps 或要求執行 content-research 的「gaps」動作時使用。此為 content-research 技能的進入點。
這項技能定義了一個名為 `cr-gaps` 的 AI 代理指令，用於執行「內容研究」(content-research) 技能中的「gaps」動作，幫助使用者識別高需求、低供應的內容缺口。其主要用途在於快速分析市場或主題中尚未被充分覆蓋但具有潛在價值的內容領域，從而指導內容策略或創作方向。技能支援常見的 AI 代理平台（如 Claude Code、Cursor 等），並透過讀取 `content-research/actions/_fast.md` 與 `gaps.md` 兩個文件來啟動快速路徑。在執行過程中，它會將使用者輸入的 `cr-gaps` 之後的文字視為動作參數，然後按照快速路徑進行規劃（三行內）、並行收集資料、執行腳本，最後根據渲染的區塊產出報告。若動作的某些階段尚未完全實作，則執行已完成的部分，並明確指出哪些階段尚在待辦。此技能僅作為進入點，不重複實作複雜的工作流程邏輯，確保結構簡潔易維護。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-gapsInstalled? 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 gaps 動作。
content-research/actions/_fast.md 以及動作檔案 content-research/actions/gaps.md。這在正常執行時已足夠；僅在步驟失敗時才開啟其他文件。cr-gaps 之後輸入的任何內容視為該動作的引數。尋找檔案路徑：安裝完成後，content-research 會是此資料夾的同層資料夾（../content-research/）。在儲存庫中，它的相對路徑是 ../../。請使用實際存在的路徑。
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 - run the complete content research workflow. Use only when the user invokes cr-full or asks for the content-research "full" 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.