- 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.pipelineThe pipeline will:
- Search GitHub for trending AI Agent projects (keyword search + org monitoring)
- Score each project with LLM on 4 dimensions: novelty, practicality, content fit, ease of use
- Present Top 3 ranked projects for user selection
- Analyze the selected project in depth (README, code, architecture)
- Generate Xiaohongshu copywriting with smart hashtags
- 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 tutorialpost.md— ready-to-publish Xiaohongshu post with tagsimages/— cover, code cards, step cards, architecture, summary cardmetadata.json— project metadata and scores
Setup
Before first use, ensure dependencies are installed:
cd {baseDir} && pip install -r requirements.txtAnd configure .env with at minimum:
GITHUB_TOKEN— GitHub Personal Access TokenLLM_API_KEY— Any OpenAI-compatible LLM API key