内容研究指令 - 撰写所选机会的详细内容简报。仅在用户调用 cr-brief 或要求“内容研究”的“brief”操作时使用。此为内容研究技能的入口点。
该技能是“内容研究(Content Research)”技能的入口点，专门用于执行 `brief` 操作。用户只需在提示中输入 `cr-brief` 并附上参数，AI 代理就会生成一份关于特定主题或机会的高度结构化内容简报。技能提供了清晰的“快速路径”，指导代理以最少的外部文件读取高效工作：读取主操作文件后进行三行规划、并行收集，然后运行脚本并输出报告。如果所有操作阶段未全部实现，代理则运行已就绪的阶段并注明待完成的部分。该技能支持 Claude Code、Cursor 和 Codex 等主流 AI 代理平台，并在 GitHub 上对开发者开源。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-briefInstalled? Explore more 写作与编辑 skills: steipete/notion, langchain-ai/langchain, bytedance/podcast-generation · View all 6 →
运行 content-research 技能的 brief 操作。
content-research/actions/_fast.md 以及操作文件 content-research/actions/brief.md。正常运行时这已足够；仅当某一步骤失败时才打开其他文档。cr-brief 后输入的任何内容视为该操作的参数。文件定位：安装后，content-research 是本文件夹的兄弟文件夹（../content-research/）。在仓库中，其位置为 ../../ 相对于本文件夹。使用存在的那一个。
此文件仅作为入口点。请勿在此重复工作流逻辑。
This repo contains 3 individual skills — each has its own dedicated page.
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.
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.
Notion CLI/API for pages, Markdown content, data sources, files, comments, search, Workers, and raw API calls.
The agent engineering platform.
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors