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.
- Installs
- 22.1K
- Last 24h
- +122
- Trending rank
- #1725
- Security
- Warn
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/tavily-ai/skills --skill tavily-research- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads tavily-research automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
# 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:
# 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
FAQ
What does the tavily-research skill do?
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.
How do I install tavily-research?
Run npx skills add https://github.com/tavily-ai/skills --skill tavily-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tavily-research/ (personal) or .claude/skills/tavily-research/ (project).
Which AI agents can use tavily-research?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is tavily-research safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains tavily-research?
It is published by tavily-ai in tavily-ai/skills, and it is an official skill from the maker of the technology it covers. It has 22,118 recorded installs.
More from tavily-ai/skills
tavily-search
tavily-ai/skills
Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.
tavily-best-practices
tavily-ai/skills
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
tavily-extract
tavily-ai/skills
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
tavily-cli
tavily-ai/skills
Set up, authenticate, update, troubleshoot, or choose between Tavily CLI web commands. Use when the user asks about the Tavily CLI, installing Tavily skills, first-time setup, authentication, keyless limits, CLI updates, or which Tavily command to use. For an ordinary web task, use the specific search, extract, map, crawl, research, or dynamic-search skill instead.
tavily-crawl
tavily-ai/skills
Crawl websites and extract content from multiple pages via the Tavily CLI. Use this skill when the user wants to crawl a site, download documentation, extract an entire docs section, bulk-extract pages, save a site as local markdown files, or says "crawl", "get all the pages", "download the docs", "extract everything under /docs", "bulk extract", or needs content from many pages on the same domain. Supports depth/breadth control, path filtering, semantic instructions, and saving each page as a local markdown file.
tavily-map
tavily-ai/skills
Discover and list all URLs on a website without extracting content, via the Tavily CLI. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", "list all pages", or "site structure". Faster than crawling — returns URLs only. Essential when you know the site but not the exact page. Combine with extract for targeted content retrieval.
Popular productivity & automation skills
agent-browser
vercel-labs/agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
lark-doc
open.feishu.cn
飞书云文档（Docx / Wiki）内容操作：读取、创建、编辑文档，插入或下载图片附件，以及操作思维笔记。用户提供文档 URL/token（包括 doubao.com 的 /docx/、/wiki/）时使用；按 URL 路径/token 而非域名路由；/page/ 是妙搭应用，非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive；随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。
lark-base
open.feishu.cn
飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式（BaseApp/AppMode 页面与组件）、Workspace 目录、workflow、角色权限、模板中心（多维表格模板分类/列表/搜索）；遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps；文件导入/导出转 lark-drive，认证/授权转 lark-shared。
lark-drive
open.feishu.cn
飞书云空间（云盘/云存储）：管理 Drive 文件和文件夹，包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签（secure labels）和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题，或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用；doubao.com 云空间 URL/token 也按资源路径和 token 路由，不回退 WebFetch。不负责：文档内容编辑（走 lark-doc）、表格/Base 表内数据操作（走 lark-sheets/lark-base）、知识空间节点/成员管理（走 lark-wiki）、原生 Markdown 文件读写/patch/diff（走 lark-markdown）。
lark-shared
open.feishu.cn
Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.
lark-im
open.feishu.cn
飞书即时通讯：收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片（Interactive Card）、监听卡片按钮回调（card.action.trigger）。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶（添加/移除/查询置顶会话）、管理标签数据、处理卡片回调时使用。