内容研究命令：分析竞争对手及其内容。仅在用户调用 cr-competitors 或请求执行 content-research "competitors" 操作时使用。这是 content-research 技能的入口点。
该技能为 AI 智能体（如 Claude Code、Cursor 或 Codex）提供了一个针对“内容研究”中 competitors 操作的高效执行入口。它通过读取预定义的 fast.md 和 competitors.md 文件来快速完成任务，遵循三步规划、并行收集、运行脚本并生成报告的轻量级工作流程。技能的核心优势在于简洁的路径指引：它明确指示智能体将用户输入作为参数接收，仅依赖两个核心文档，在文件定位上兼容本地安装和仓库结构两种场景（分别位于 ../content-research/ 或 ../../ 路径）。这显著降低了智能体在多步骤操作中的认知负担，确保执行过程标准化且可重复。如果某个阶段未完全实现，技能会要求智能体运行已实现部分并明确告知遗漏点，从而保持透明性。该技能支持所有主流 AI 编码代理平台，通过其明确的入口点设计和最小化文档依赖，使得内容竞争对手分析工作流在代理环境中变得简洁高效。
npx skills add https://github.com/fdevaibhavpal/ai-agent-skills/tree/HEAD/content-research/commands/cr-competitorsInstalled? 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 competitors 操作。
content-research/actions/_fast.md 和动作文件 content-research/actions/competitors.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-competitors 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 是该文件夹的兄弟目录（../content-research/）。在仓库中，它位于当前目录的 ../../ 处。使用实际存在的路径。
此文件仅为入口点。请勿在此处重复工作流逻辑。
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 - 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.