Tavily Research Agent Skill
| 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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/tavily-ai/skills --skill tavily-researchWhat it tells the agent to use
found in the instruction textThe instruction itself
9 sections, as written by the authortavily research
AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.
Before running any command
If tvly is not found on PATH, install it first:
curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login
Do not skip this step or fall back to other tools.
See tavily-cli for alternative install methods and auth options.
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
# 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.md
# 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 output to 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:
# 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
--streamto see progress in real-time. - Use
--model profor complex comparisons or multi-faceted topics. - Use
--output-schemato get structured JSON output matching a custom schema. - For quick facts, use
tvly searchinstead — research is for deep synthesis. - Read from stdin:
echo "query" | tvly research - --json
See also
- tavily-search — quick web search for simple lookups
- tavily-crawl — bulk extract from a site for your own analysis
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How to use it
Take tavily-ai/tavily-research from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.