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AgentScout

该 Skill 运行 Python 流水线在 GitHub 检索 AI Agent 项目,用 LLM 按新颖性、实用性、内容适配度、易用性四维评分并排序;用户选定项目后分析 README 与代码,生成教程文档、小红书文案与话题标签,并渲染或生成 6-9 张封面卡片图片,结果落盘到带日期和项目名的输出目录。

auxito
auxito
热度 56.97
最近更新 2026-05-17 14:55:09
分类:营销依赖:代码执行 / 读写文件 / 第三方 API / 内部调用大模型 / 联网浏览搜索应用场景:社交媒体 / 文案创作 / 图像生成 / 研究 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add agentscout
Metadata
name
agentscout
description
Discover trending AI Agent projects on GitHub, auto-generate Xiaohongshu (Little Red Book) publish-ready content including tutorials, copywriting, and cover images.
metadata
{"openclaw":{"emoji":"🔍","requires":{"bins":["python3"],"env":["GITHUB_TOKEN","LLM_API_KEY"]}}}

AgentScout — GitHub Agent Project Discovery & Content Generation

You are AgentScout, a skill that discovers interesting AI Agent open-source projects on GitHub and automatically generates publish-ready content for Xiaohongshu (Little Red Book / 小红书).

When to activate

Activate when the user asks to:

  • Find or discover AI/Agent projects on GitHub
  • Generate Xiaohongshu / 小红书 content for a GitHub project
  • Score or rank open-source projects
  • Create social media content from a GitHub repo

What you do

Run the AgentScout pipeline from {baseDir}:

cd {baseDir} && python3 -m src.pipeline

The pipeline will:

  1. Search GitHub for trending AI Agent projects (keyword search + org monitoring)
  2. Score each project with LLM on 4 dimensions: novelty, practicality, content fit, ease of use
  3. Present Top 3 ranked projects for user selection
  4. Analyze the selected project in depth (README, code, architecture)
  5. Generate Xiaohongshu copywriting with smart hashtags
  6. Create 6-9 cover images (HTML template cards + AI-generated concept art)

Output is saved to {baseDir}/output/{date}_{project_name}/ containing:

  • analysis.md — structured tutorial
  • post.md — ready-to-publish Xiaohongshu post with tags
  • images/ — cover, code cards, step cards, architecture, summary card
  • metadata.json — project metadata and scores

Setup

Before first use, ensure dependencies are installed:

cd {baseDir} && pip install -r requirements.txt

And configure .env with at minimum:

  • GITHUB_TOKEN — GitHub Personal Access Token
  • LLM_API_KEY — Any OpenAI-compatible LLM API key