内容研究命令 - 运行完整的内容研究工作流程。仅在用户调用 cr-full 或要求“内容研究”的“完整”操作时使用。这是内容研究技能的入口点。
这是一个用于 AI 代理的内容研究技能，提供“完整”工作流程的入口点。当用户输入 `cr-full` 或明确要求执行“内容研究”的“完整”操作时，该技能会启动。它通过读取指定的操作文件（如 `_fast.md` 和 `full.md`）来执行研究流程，遵循快速路径：在三行内规划、并行收集信息、运行脚本，并根据渲染的模块生成报告。该技能支持 Claude Code 等 AI 代理工具，其中 `content-research` 文件夹作为同级或上级目录存在。如果某些阶段尚未实现，它会运行已实现的部分并说明待办事项，确保工作流程透明且高效。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-fullInstalled? 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 full 操作。
content-research/actions/_fast.md 和操作文件 content-research/actions/full.md。对于正常执行，这些就足够了；只有当某个步骤失败时才打开其他文档。cr-full 之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 是本文件夹的同级文件夹 (../content-research/)。在仓库中，它位于此处的 ../../。使用存在的那一个。
此文件仅为入口点。请勿在此重复工作流程逻辑。
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 - 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.