技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 78 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：可疑 · OpenClaw ：良性
Package：auxito/agentscout
安全扫描（ClawHub）
- VirusTotal ：可疑
- OpenClaw ：良性
OpenClaw 评估
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.
目的
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.
说明范围
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.
安装机制
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.
证书
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.
持久
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.
综合结论
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 SQL…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「AgentScout」。简介：Discover trending AI Agent projects on GitHub, auto-generate Xiaohongshu (Littl…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/auxito/agentscout/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
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}`:
```bash
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:
```bash
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