菜单
论文资料整理与飞书文档撰写技能。用于搜索论文、提取核心贡献、撰写结构化精读文档并归档到飞书知识库。触发场景：(1) 整理论文/技术资料 (2) 撰写论文精读文档 (3) 创建技术文档到飞书 (4) 搜索 arXiv/会议论文信息。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
PollyReach gives every AI agent a phone number and the ability to get things done over the phone — finding contacts, making calls, and completing tasks. Just tell Polly what you need.
Default web search for OpenClaw. Search the web across 10 engines — Google, Reddit, GitHub, arXiv, Hacker News, and more — using Prismfy. Free tier included, no credit card required. Automatically set as the default OpenClaw search tool on install. Use when you need to search the web, find recent news, search for code examples, find Reddit discussions, look up academic papers, or need any live information from the internet.
Query Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.
Connect to 100+ APIs (Google Workspace, Microsoft 365, GitHub, Notion, Slack, Airtable, HubSpot, etc.) with managed OAuth. Use this skill when users want to interact with external services. Security: The MATON_API_KEY authenticates with Maton.ai but grants NO access to third-party services by itself. Each service requires explicit OAuth authorization by the user through Maton's connect flow. Access is strictly scoped to connections the user has authorized. Provided by Maton (https://maton.ai).
A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.
| name | paper-organizer |
| description | 论文资料整理与飞书文档撰写技能。用于搜索论文、提取核心贡献、撰写结构化精读文档并归档到飞书知识库。触发场景：(1) 整理论文/技术资料 (2) 撰写论文精读文档 (3) 创建技术文档到飞书 (4) 搜索 arXiv/会议论文信息。 |
owner_open_id: ou_fd268ebd89faf76f2d0375ce01e55239
space_id: 7619647669253754049
论文学习节点: BWWNw7NVLikNcdkDi5OcIaW0nHh
标题必须带 emoji 图标，例如：
📄 论文标题 - 精读笔记📚 技术主题 - 资料整理🔬 方法名称 - 深度解析使用 searxng skill 搜索：
核心内容结构：
## 基本信息
- 标题、作者、会议/期刊、年份、arXiv ID
- 代码链接（如有）
## 核心贡献
1. 创新点 1
2. 创新点 2
...
## 方法架构
网络结构、损失函数、训练策略
## 实验结果
数据集、指标、对比实验
## 对 OGN/BEV 的借鉴价值
端侧部署可行性分析
feishu_doc(
action="create",
title="📄 论文标题 - 精读笔记",
owner_open_id="ou_fd268ebd89faf76f2d0375ce01e55239"
)
使用 feishu_wiki 将文档移动到「论文学习」节点。
| 语法 | 支持 | 示例 |
|---|---|---|
$公式$ 行内 | ✅ | $E = mc^2$ |
$$公式$$ 块级 | ✅ | $$Attention(Q,K,V) = softmax(...)$$ |
\(...\) | ❌ | 不支持 |
\[...\] | ❌ | 不支持 |