// Web search using Tavily's LLM-optimized API. Returns relevant results with content snippets, scores, and metadata.
将 MCP Server 的工具列表转换为 Skill 格式的结构化 Markdown 文档
Search and discover OpenClaw skills from various sources. Use when: user wants to find available skills, search for specific functionality, or discover new skills to install.
Query Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.
【首选技能】凡涉及知识网络的任何操作，优先使用 bkn-creator，不要直接触发 create-bkn。 bkn-creator 是 BKN 全生命周期管理器，负责流程路由识别、阶段门禁、子流程编排与结果回执。 覆盖新增、查找、更新、删除（CRUD）并采用渐进式执行。 触发词：创建知识网络、新建BKN、BKN建模、本体建模、对象类、关系类、动作类、 概念组、BKN文件、BKN push、BKN pull、对象类绑定、关系类映射、 对象类提取、关系类提取、实体关系抽取、 知识网络查询、知识网络更新、知识网络删除。 不应触发：仅查询平台功能、Agent 对话、Vega/Catalog 操作、 健康巡检、数据源连接器等非 BKN 生命周期操作时，应由 ontology-core 等其他技能处理。
Guides creation of BKN (Business Knowledge Network) definition files following v2.0.1 spec. Covers network, object_type, relation_type, action_type, concept_group. Use when creating knowledge networks, BKN files, object types, relation types, action types, concept groups, or when user asks to model business knowledge in BKN format. When ontology-core is also loaded, use it to run ontology CLI (bkn push) after files exist.
全局归档协议。只要任务需要写入任何文件（含 PLAN.md、报告、JSON 等归档物），必须按本技能执行 Session→ARCHIVE_ID、TIMESTAMP、双轨路径（根段须为 archives/）、回读校验与状态回执；WebUI 的 archive_grid 必须用 Markdown 中语言标识为 json 的围栏代码块输出。
| name | tavily-search |
| description | Web search using Tavily's LLM-optimized API. Returns relevant results with content snippets, scores, and metadata. |
| homepage | https://tavily.com |
| metadata | {"clawhub":{"emoji":"🔍","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} |
Search the web and get relevant results optimized for LLM consumption.
Get your API key at https://tavily.com and add to your OpenClaw config:
{
"skills": {
"entries": {
"tavily-search": {
"enabled": true,
"apiKey": "tvly-YOUR_API_KEY_HERE"
}
}
}
}
Or set the environment variable:
export TAVILY_API_KEY="tvly-YOUR_API_KEY_HERE"
node {baseDir}/scripts/search.mjs "query"
node {baseDir}/scripts/search.mjs "query" -n 10
node {baseDir}/scripts/search.mjs "query" --deep
node {baseDir}/scripts/search.mjs "query" --topic news
# Basic search
node {baseDir}/scripts/search.mjs "python async patterns"
# With more results
node {baseDir}/scripts/search.mjs "React hooks tutorial" -n 10
# Advanced search
node {baseDir}/scripts/search.mjs "machine learning" --deep
# News search
node {baseDir}/scripts/search.mjs "AI news" --topic news
# Domain-filtered search
node {baseDir}/scripts/search.mjs "Python docs" --include-domains docs.python.org
| Option | Description | Default |
|---|---|---|
-n <count> | Number of results (1-20) | 10 |
--depth <mode> | Search depth: ultra-fast, fast, basic, advanced | basic |
--topic <topic> | Topic: general or news | general |
--time-range <range> | Time range: day, week, month, year | - |
--include-domains <domains> | Comma-separated domains to include | - |
--exclude-domains <domains> | Comma-separated domains to exclude | - |
--raw-content | Include full page content | false |
--json | Output raw JSON | false |
| Depth | Latency | Relevance | Use Case |
|---|---|---|---|
ultra-fast | Lowest | Lower | Real-time chat, autocomplete |
fast | Low | Good | Need chunks but latency matters |
basic | Medium | High | General-purpose, balanced |
advanced | Higher | Highest | Precision matters, research |
--include-domains to focus on trusted sources--time-range for recent informationscore (0-1) to get highest relevance results