AgentScout

Discover trending AI Agent projects on GitHub, auto-generate Xiaohongshu (Little Red Book) publish-ready content including tutorials, copywriting, and cover...

Security Scan

AgentScout's code, instructions, and required secrets are consistent with its stated purpose of discovering GitHub agent projects and generating Xiaohongshu content, but it will send README/code snippets to whatever LLM endpoint you configure, so treat the API key and GitHub token carefully.

Purpose & Capability

Name/description match the implementation: the package contains GitHub searchers, a scorer, analyzers, content generators, and image/card generators. Required binaries (python3) and env vars (GITHUB_TOKEN, LLM_API_KEY) are appropriate and necessary for searching GitHub and calling an LLM. Optional IMAGE_API_KEY aligns with AI image generation.

Instruction Scope

The runtime instructions (and code) explicitly fetch README, file trees, and key source files (up to several files and snippets) and pass those to the configured LLM for scoring and tutorial generation. This behavior is consistent with the purpose but means repository contents (including possibly sensitive code) will be transmitted to the LLM endpoint you configure.

Install Mechanism

No formal install spec is provided; the README and SKILL.md ask you to pip install -r requirements.txt. The requirements are from standard PyPI packages (PyGithub, openai, playwright, etc.). Playwright install (optional) will download browser binaries, which is typical but worth noting.

Credentials

Only GITHUB_TOKEN and LLM_API_KEY are required; both are proportional to the functionality. IMAGE_API_KEY is optional. There are no unrelated secrets or extraneous credential requests in requires.env. You should still scope tokens minimally (e.g., public_repo or least privilege) and avoid using org-wide or highly privileged tokens.

Persistence & Privilege

Skill is not always-enabled and does not request elevated platform privileges. It writes an SQLite DB under the project data/ path and writes outputs under output/, which is reasonable for local use. It does not modify other skills or system-wide agent settings.

Assessment

Things to consider before installing or running AgentScout: - Expect that README content and code snippets will be sent to the configured LLM endpoint (LLM_BASE_URL). Do not point LLM_BASE_URL to an untrusted service if repository content is sensitive. - Use least-privilege GitHub tokens (avoid full org/admin scopes). Prefer tokens limited to public repo access if you only need public repos. - The tool writes outputs to output/ and a local SQLite DB at data/agentscout.db in the repo root; run in a sandbox or isolated directory if you want to limit persistence. - Optional IMAGE_API_KEY defaults to a third-party endpoint (siliconflow). If you supply an image key, confirm the provider and its privacy policy. - The README requests pip install -r requirements.txt; review requirements and consider installing in a virtualenv/container. Playwright (optional) will download browser binaries when installed. - If you are concerned about leaking proprietary code, either restrict the search scope to public repositories, run the code with a dummy/limited LLM endpoint, or audit/modify src/utils/llm_client.py to confirm exactly what gets sent. - Overall the skill appears internally coherent for its stated purpose; proceed if you are comfortable with the data-sharing implications and have limited token scope and an isolated runtime environment.

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