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tavily-research — Claude Code skill: what it does, how to install

Runs deep web research via the Tavily CLI and returns a structured report with source citations. Useful for market reviews and multi-source technical comparisons.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add tavily-ai/skills --skill tavily-research -a claude-code
Open on GitHub

What tavily-research does

The skill executes the tvly research command directly through Bash. Instead of returning raw search hits, it gathers pages across the web, synthesizes findings, and outputs a cited document. Execution takes between 30 and 120 seconds. You can select the mini model for single questions or pro for multi-angle topics, with citation styles such as numbered, apa, mla, and chicago.

Authentication is required before running any query. The CLI uses browser OAuth by default, or falls back to a provided TAVILY_API_KEY environment variable in headless setups. Without prior authentication, the skill fails immediately. You can track long-running queries in real time with --stream or decouple them using the --no-wait flag and polling.

Using this skill for simple lookups wastes time, because standard queries through tvly search answer quick questions much faster.

What it helps with

  • build an analytical market report grounded in cited web sources
  • compare competing frameworks or products across multiple angles
  • export structured research findings to JSON with a custom schema

Example prompts for the agent

  • Research the competitive landscape of AI code assistants and provide source citations.
  • Run a deep pro analysis on electric vehicle market trends and save it to a JSON file.
  • Compare AI agent frameworks with real-time stream output.

How to install tavily-research

1Skills CLI (Vercel)

npx skills add tavily-ai/skills --skill tavily-research -a claude-code

2Manually: put the files in place

~/.claude/skills/tavily-research/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/tavily-research/

From the same set6

The whole set →

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Questions about tavily-research

How to install tavily-research in Claude Code?

Run npx skills add tavily-ai/skills --skill tavily-research -a claude-code in your terminal.

What does tavily-research do?

It invokes the tvly CLI to gather web data, analyze multiple pages, and generate a synthesized report with citations. The run takes 30 to 120 seconds depending on the chosen model.

How do the mini and pro models differ in tavily-research?

The mini model handles single-topic questions in about 30 seconds. The pro model evaluates topics from multiple angles and takes 60 to 120 seconds for comprehensive comparisons.

Source text

SKILL.md · MIT
---
name: tavily-research
description: |
  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.
allowed-tools: Bash(tvly *)
---

# 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](../tavily-cli/SKILL.md#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](../tavily-cli/SKILL.md): 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`

## See also

- [tavily-search](../tavily-search/SKILL.md) — quick web search for simple lookups
- [tavily-crawl](../tavily-crawl/SKILL.md) — bulk extract from a site for your own analysis

© tavily-ai · MIT · tavily-ai/skills

See also

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