分析競爭創作者及其內容。僅在使用者調用 cr-competitors 或要求執行 content-research 的「competitors」動作時使用。此為 content-research 技能的入口點。
此技能（名為 cr-competitors）是 content-research 技能中「competitors」動作的入口點。它能幫助使用者分析競爭創作者及其內容策略。當使用者明確要求執行競爭對手分析（例如輸入 cr-competitors）時，此技能會被觸發。它遵循快速路徑：以三行文字規劃、平行收集資料、執行腳本、最後根據渲染區塊撰寫報告。若某些階段尚未實現，則會執行已完成的階段並列出待辦項目。此技能支援 Claude Code 等 AI 代理平台，透過讀取 `content-research/actions/_fast.md` 和 `competitors.md` 文件來獲取執行邏輯，其檔案位置可位於安裝後的 `../content-research/` 目錄或儲存庫中的 `../../` 路徑。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-competitorsInstalled? 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 competitors 動作。
content-research/actions/_fast.md 以及動作檔案 content-research/actions/competitors.md。一般執行時只需這些；僅當某個步驟失敗時才打開其他文件。cr-competitors 後的任何內容視為該動作的引數。檔案定位：安裝後，content-research 是本資料夾的同層資料夾（../content-research/）。在儲存庫中，它位於 ../../。使用任何存在的路徑。
此文件僅為入口點。請勿在此重複工作流程邏輯。
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 - 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.
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.