agents/agent-reach/SKILL.md
Give your AI agent eyes to see the entire internet. 17 platforms via CLI, MCP, curl, and Python scripts. Zero config for 8 channels. 【路由方式】SKILL.md 包含路由表和常用命令,复杂场景需按需阅读对应分类的 references/*.md。 分类:search / social (小红书/抖音/微博/推特/B站/V2EX/Reddit) / career(LinkedIn) / dev(github) / web(网页/文章/公众号/RSS) / video(YouTube/B站/播客). Use when user asks to search, read, or interact on any supported platform, shares a URL, or asks to search the web.
npx skillsauth add adminlove520/xiaoxi-skills agent-reachInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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17 平台工具集合。根据用户意图选择对应分类。
| 用户意图 | 分类 | 详细文档 | |---------|------|---------| | 网页搜索/代码搜索 | search | references/search.md | | 小红书/抖音/微博/推特/B站/V2EX/Reddit | social | references/social.md | | 招聘/职位/LinkedIn | career | references/career.md | | GitHub/代码 | dev | references/dev.md | | 网页/文章/公众号/RSS | web | references/web.md | | YouTube/B站/播客字幕 | video | references/video.md |
# Exa 网页搜索
mcporter call 'exa.web_search_exa(query: "query", numResults: 5)'
# 通用网页阅读
curl -s "https://r.jina.ai/URL"
# GitHub 搜索
gh search repos "query" --sort stars --limit 10
# Twitter 搜索
twitter search "query" --limit 10
# YouTube/B站字幕
yt-dlp --write-sub --skip-download -o "/tmp/%(id)s" "URL"
# Reddit 搜索
rdt search "query" --limit 10
# Reddit 读帖 + 评论
rdt read POST_ID
# V2EX 热门
curl -s "https://www.v2ex.com/api/topics/hot.json" -H "User-Agent: agent-reach/1.0"
# 检查可用 channel
agent-reach doctor
# 查看所有 MCP 服务
mcporter_list_servers()
不要在 agent workspace 创建文件。 使用 /tmp/ 存放临时输出,~/.agent-reach/ 存放持久数据。
根据用户需求,阅读对应的详细文档:
如果某个 channel 需要配置,获取安装指南: https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md
用户只需提供 cookies,其他配置由 agent 完成。
data-ai
Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with adaptive scheduling, and export/import decks as CSV.
development
Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.
development
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
devops
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.