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.
npx skills add tavily-ai/skills --skill tavily-research -a claude-codeWhat 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-code2Manually: put the files in place
~/.claude/skills/tavily-research/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/tavily-research/
1Skills CLI (Vercel)
npx skills add tavily-ai/skills --skill tavily-research -a codex2Manually: put the files in place
.agents/skills/tavily-research/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add tavily-ai/skills --skill tavily-research -a cursor2Manually: put the files in place
.cursor/skills/tavily-research/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install tavily-ai/skills tavily-research2Manually: put the files in place
.github/skills/tavily-research/SKILL.md1Skills CLI (Vercel)
npx skills add tavily-ai/skills --skill tavily-research -a gemini-cli2Platform CLI
gemini skills install https://github.com/tavily-ai/skills --path skills/tavily-researchManually: put the files in place
.devin/skills/tavily-research/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add tavily-ai/skills --skill tavily-research -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set6
The whole set →Similar
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
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