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Tavily

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intellectronica
tavily

Use this skill for web search, extraction, mapping, crawling, and research via Tavily’s REST API when web searches are needed and no built-in tool is available, or when Tavily’s LLM-friendly format is beneficial.

Overview

Publisherintellectronica
Repositoryagent-skills
Skill nametavily
Stars
297
Forks
27
Bundled files
Instructions only
LicenseCC0-1.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by intellectronica on GitHub. Read the source before you install it.

Installation

Install the Tavily AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/intellectronica/agent-skills/tree/main/plugins/tavily/skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/intellectronica/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/plugins/tavily/skills .claude/skills/tavily
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tavily in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tavily on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tavily is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Tavily

Purpose

Provide a curl-based interface to Tavily’s REST API for web search, extraction, mapping, crawling, and optional research. Return structured results suitable for LLM workflows and multi-step investigations.

When to Use

  • Use when a task needs live web information, site extraction, mapping, or crawling.
  • Use when web searches are needed and no built-in tool is available, or when Tavily’s LLM-friendly output (summaries, chunks, sources, citations) is beneficial.
  • Use when a task requires structured search results, extraction, or site discovery from Tavily.

Required Environment

  • Require TAVILY_API_KEY in the environment.
  • If TAVILY_API_KEY is missing, prompt the user to provide the API key before proceeding.

Base URL and Auth

  • Base URL: https://api.tavily.com
  • Authentication: Authorization: Bearer $TAVILY_API_KEY
  • Content type: Content-Type: application/json
  • Optional project tracking: add X-Project-ID: <project-id> if project attribution is needed.

Tool Mapping (Tavily REST)

1) search → POST /search

Use for web search with optional answer and content extraction.

Recommended minimal request:

bash
curl -sS -X POST "https://api.tavily.com/search" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "query": "<query>",
    "search_depth": "basic",
    "max_results": 5,
    "include_answer": true,
    "include_raw_content": false,
    "include_images": false
  }'

Key parameters (all optional unless noted):

  • query (required): search text
  • search_depth: basic | advanced | fast | ultra-fast
  • chunks_per_source: 1–3 (advanced only)
  • max_results: 0–20
  • topic: general | news | finance
  • time_range: day|week|month|year|d|w|m|y
  • start_date, end_date: YYYY-MM-DD
  • include_answer: false | true | basic | advanced
  • include_raw_content: false | true | markdown | text
  • include_images: boolean
  • include_image_descriptions: boolean
  • include_favicon: boolean
  • include_domains, exclude_domains: string arrays
  • country: country name (general topic only)
  • auto_parameters: boolean
  • include_usage: boolean

Expected response fields:

  • answer (if requested), results[] with title, url, content, score, raw_content (optional), favicon (optional)
  • response_time, usage, request_id

2) extract → POST /extract

Use for extracting content from specific URLs.

bash
curl -sS -X POST "https://api.tavily.com/extract" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "urls": ["https://example.com/article"],
    "query": "<optional intent for reranking>",
    "chunks_per_source": 3,
    "extract_depth": "basic",
    "format": "markdown",
    "include_images": false,
    "include_favicon": false
  }'

Key parameters:

  • urls (required): array of URLs
  • query: rerank chunks by intent
  • chunks_per_source: 1–5 (only when query provided)
  • extract_depth: basic | advanced
  • format: markdown | text
  • timeout: 1–60 seconds
  • include_usage: boolean

Expected response fields:

  • results[] with url, raw_content, images, favicon
  • failed_results[], response_time, usage, request_id

3) map → POST /map

Use for generating a site map (URL discovery only).

bash
curl -sS -X POST "https://api.tavily.com/map" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "url": "https://docs.tavily.com",
    "max_depth": 1,
    "max_breadth": 20,
    "limit": 50,
    "allow_external": true
  }'

Key parameters:

  • url (required)
  • instructions: natural language guidance (raises cost)
  • max_depth: 1–5
  • max_breadth: 1+
  • limit: 1+
  • select_paths, select_domains, exclude_paths, exclude_domains: arrays of regex strings
  • allow_external: boolean
  • timeout: 10–150 seconds
  • include_usage: boolean

Expected response fields:

  • base_url, results[] (list of URLs), response_time, usage, request_id

4) crawl → POST /crawl

Use for site traversal with built-in extraction.

bash
curl -sS -X POST "https://api.tavily.com/crawl" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "url": "https://docs.tavily.com",
    "instructions": "Find all pages about the Python SDK",
    "max_depth": 1,
    "max_breadth": 20,
    "limit": 50,
    "extract_depth": "basic",
    "format": "markdown",
    "include_images": false
  }'

Key parameters:

  • url (required)
  • instructions: optional; raises cost and enables chunks_per_source
  • chunks_per_source: 1–5 (only with instructions)
  • max_depth, max_breadth, limit: same as map
  • extract_depth: basic | advanced
  • format: markdown | text
  • include_images, include_favicon, allow_external
  • timeout: 10–150 seconds
  • include_usage: boolean

Expected response fields:

  • base_url, results[] with url, raw_content, favicon
  • response_time, usage, request_id

Optional Research Workflow (Deep Investigation)

Use when a query needs multi-step analysis and citations.

create research task → POST /research

bash
curl -sS -X POST "https://api.tavily.com/research" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "input": "<research question>",
    "model": "auto",
    "stream": false,
    "citation_format": "numbered"
  }'

Expected response fields:

  • request_id, created_at, status (pending), input, model, response_time

get research status → GET /research/{request_id}

bash
curl -sS -X GET "https://api.tavily.com/research/<request_id>" \
  -H "Authorization: Bearer $TAVILY_API_KEY"

Expected response fields:

  • status: completed
  • content: report text or structured object
  • sources[]: { title, url, favicon }

streaming research (SSE)

Set "stream": true in the POST body and use curl with -N to stream events:

bash
curl -N -X POST "https://api.tavily.com/research" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{"input":"<question>","stream":true,"model":"pro"}'

Handle SSE events (tool calls, tool responses, content chunks, sources, done).

Usage Notes

  • Treat search, extract, map, and crawl as the primary endpoints for discovery and content retrieval.
  • Return structured results with URLs, titles, and summaries for easy downstream use.
  • Default to conservative parameters (search_depth: basic, max_results: 5) unless deeper recall is needed.
  • Reuse consistent request bodies across calls to keep results predictable.

Error Handling

  • If any request returns 401/403, prompt for or re-check TAVILY_API_KEY.
  • If timeouts occur, reduce max_depth/limit or use search_depth: basic.
  • If responses are too large, lower max_results or chunks_per_source.

Frequently asked questions

What does the Tavily AI skill do?

Use this skill for web search, extraction, mapping, crawling, and research via Tavily’s REST API when web searches are needed and no built-in tool is available, or when Tavily’s LLM-friendly format is beneficial.

Why use Tavily on TypingMind?

Because you install it once and use it with any model. Tavily is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tavily in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/intellectronica/agent-skills/tree/main/plugins/tavily/skills. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tavily?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tavily?

As many as you like. As long as a model supports skills, you can use Tavily with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tavily AI skill free?

Yes. It is published on GitHub by intellectronica under the CC0-1.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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