Tavily Research

Organization

tavily-ai

tavily-research

Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

Overview

Publisher	tavily-ai
Repository	`skills`
Skill name	`tavily-research`
Stars	486
Forks	40
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 tavily-ai on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/tavily-ai/skills/tree/main/skills/tavily-research
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.

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/tavily-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/tavily-research .claude/skills/tavily-research

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tavily Research 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 Research 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 Research 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 research

AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.

Before running

Research requires authentication. Run the requested command directly when tvly is already authenticated; do not add a status check to every invocation.

If tvly is missing, follow the tavily-cli setup. If an installed CLI reports an authentication error, use tvly login for authentication only, or tvly init --skip-skills when guided verification is also useful. Browser-based OAuth is preferred when an interactive user can complete it. --no-browser prints the sign-in link instead of opening it, but still waits for a localhost callback. In an unattended agent or CI environment, leave authentication to the user or use a securely provided TAVILY_API_KEY. Do not start a second login immediately after guided setup has completed.

When to use

You need comprehensive, multi-source analysis
The user wants a comparison, market report, or literature review
Quick searches aren't enough — you need synthesis with citations
Step 5 in the workflow: search → extract → map → crawl → research

Quick start

bash
# Basic research (waits for completion)
tvly research "competitive landscape of AI code assistants"

# Pro model for comprehensive analysis
tvly research "electric vehicle market analysis" --model pro

# Stream results in real-time
tvly research "AI agent frameworks comparison" --stream

# Save report to file
tvly research "fintech trends 2025" --model pro -o fintech-report.json

# JSON output for agents
tvly research "quantum computing breakthroughs" --json

Options

Option	Description
`--model`	`mini`, `pro`, or `auto` (default)
`--stream`	Stream results in real-time
`--no-wait`	Return request_id immediately (async)
`--output-schema`	Path to JSON schema for structured output
`--citation-format`	`numbered`, `mla`, `apa`, `chicago`
`--poll-interval`	Seconds between checks (default: 10)
`--timeout`	Max wait seconds (default: 600)
`-o, --output`	Save the JSON response to a file
`--json`	Structured JSON output

Model selection

Model	Use for	Speed
`mini`	Single-topic, targeted research	~30s
`pro`	Comprehensive multi-angle analysis	~60-120s
`auto`	API chooses based on complexity	Varies

Rule of thumb: "What does X do?" → mini. "X vs Y vs Z" or "best way to..." → pro.

Async workflow

For long-running research, you can start and poll separately:

bash
# Start without waiting
tvly research "topic" --no-wait --json    # returns request_id

# Check status
tvly research status <request_id> --json

# Wait for completion
tvly research poll <request_id> --json -o result.json

Tips

Research takes 30-120 seconds — use --stream to see progress in real-time.
Use --model pro for complex comparisons or multi-faceted topics.
Use --output-schema to get structured JSON output matching a custom schema.
For quick facts, use tvly search instead — research is for deep synthesis.
Read from stdin: echo "query" | tvly research - --json

Frequently asked questions

What does the Tavily Research AI skill do?

Why use Tavily Research on TypingMind?

Because you install it once and use it with any model. Tavily Research 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 Research in TypingMind?

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

Which AI models can use Tavily Research?

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 Research?

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

Is the Tavily Research AI skill free?

Yes. It is published on GitHub by tavily-ai under the MIT 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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Tavily Research

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

tavily research

Before running

When to use

Quick start

Options

Model selection

Async workflow

Tips

See also

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

tavily research

Before running

When to use

Quick start

Options

Model selection

Async workflow

Tips

See also

Frequently asked questions

Related AI Skills

Set up your own AI workspace now