内容研究命令 - 发现高需求、低供给的内容空白。仅在用户调用 cr-gaps 或要求执行内容研究的“差距”操作时使用。这是内容研究技能的入口。
该技能是内容研究（content-research）工具中“差距分析”（gaps）操作的入口点，专为 AI 代理设计，用于高效发现高需求、低供给的内容空白。其工作流程遵循快速路径：首先规划三个核心步骤，然后并行收集必要数据，运行脚本，最后根据已渲染的模块生成报告。如果某些操作阶段尚未完全实现，代理会执行已完成的步骤并明确列出待办事项。该技能支持自动定位相关文件（可以是安装后的同级文件夹 `../content-research/` 或仓库中的 `../../` 路径），并仅作为入口，不重复实现详细的工作流逻辑。适用于 Claude Code、Cursor 等支持技能机制的 AI 编程助手。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-gapsInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
运行 content-research 技能的 gaps 操作。
content-research/actions/_fast.md 和操作文件 content-research/actions/gaps.md。正常运行时只需这些；仅在步骤失败时才打开其他文档。cr-gaps 后输入的任何内容视为该操作的参数。定位文件：安装后，content-research 是该文件夹的同级文件夹（../content-research/）。在仓库中，它位于此文件夹的 ../../ 目录。使用实际存在的路径。
此文件仅为入口。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 - 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.