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Skills/Git & Pull Requests
tavily-ai avatar

Tavily Research

tavily-ai/skillsOfficial

The tavily-research skill conducts comprehensive AI-powered research on complex topics, synthesizing information from multiple web sources and returning structured reports with citations. It serves users who need in-depth analysis, comparisons, market reports, or literature reviews rather than quick fact-finding. The skill solves the problem of gathering, analyzing, and citing multiple sources in a single command, with options for different model sizes, citation formats, and async workflows.

installs
22.1K
GitHub stars
491
last updated
Aug 21, 2026
View on GitHubSave

Install

$ npx -y skills add tavily-ai/skills --skill tavily-research --agent claude-code

Installs into .claude/skills of the current project.

Files

Tavily research

Use this when the answer has to be built from many sources. A single fact or answer to find is a search: follow tavily-search instead.

Search and read pages as tavily-search describes. Load it only if it isn't loaded yet.

Steps

1. Ask only when the answer would change the research

Ask when a term has two meanings, the user assumed a place, or a name is two different entities. Otherwise start.

2. Split the question

Split into subtopics that do not overlap and that together cover the question. Carry the user's limits with every subtopic: dates, places, and what is out of scope. Use 3 subtopics when you are unsure, 4 for a comparison with separate criteria, 5 for separate entities, regions or domains, and more only for a large set.

3. Research the subtopics together

With subagents: choose a title of 3 to 6 words, create research_notes/{title}/ and reports/ in the working directory, and start one general-purpose subagent per subtopic in the same turn. Give each the subtopic, the key questions, the user's limits, the absolute path of its notes file, and this first step: read references/researcher.md at its absolute path next to this skill.

On your own: run each subtopic's searches in parallel, as tavily-search describes, and extract the main sources for each. Keep the findings and their URLs in your context. Don't write notes files.

4. Fill thin parts, then write

Before writing, run another round for any part of the question that would leave its section thin.

5. Write and deliver the answer

Follow references/report-writer.md. On your own, write it once, as your reply. Save it to reports/{title}.md only when the user asked for a file, and then give the path after the report. With subagents, have one agent write reports/{title}.md from the notes, then give the user the full report in your reply, followed by its path.

If this conversation already produced a report on the subject, build on it: research only what is new or needs checking again.

SKILL.md on GitHub

Sponsors

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Details

Repository
github.com/tavily-ai/skills
Categories
Git & Pull Requests, Code Review & Quality, AI & Agent Building, Data Science & ML, CLI & Terminal

More from tavily-ai/skills

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  • Research6.5K
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