tavily

intellectronica/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/intellectronica/agent-skills --skill tavily
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summary

Provide a curl-based interface to Tavily’s REST API for web search, extraction, mapping, crawling, and optional research. Return structured results suitable for LLM workflows and multi-step investigations.

skill.md

Tavily

Purpose

Provide a curl-based interface to Tavily’s REST API for web search, extraction, mapping, crawling, and optional research. Return structured results suitable for LLM workflows and multi-step investigations.

When to Use

  • Use when a task needs live web information, site extraction, mapping, or crawling.
  • Use when web searches are needed and no built-in tool is available, or when Tavily’s LLM-friendly output (summaries, chunks, sources, citations) is beneficial.
  • Use when a task requires structured search results, extraction, or site discovery from Tavily.

Required Environment

  • Require TAVILY_API_KEY in the environment.
  • If TAVILY_API_KEY is missing, prompt the user to provide the API key before proceeding.

Base URL and Auth

  • Base URL: https://api.tavily.com
  • Authentication: Authorization: Bearer $TAVILY_API_KEY
  • Content type: Content-Type: application/json
  • Optional project tracking: add X-Project-ID: <project-id> if project attribution is needed.

Tool Mapping (Tavily REST)

1) search → POST /search

Use for web search with optional answer and content extraction.

Recommended minimal request:

curl -sS -X POST "https://api.tavily.com/search" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "query": "<query>",
    "search_depth": "basic",
    "max_results": 5,
    "include_answer": true,
    "include_raw_content": false,
    "include_images": false
  }'

Key parameters (all optional unless noted):

  • query (required): search text
  • search_depth: basic | advanced | fast | ultra-fast
  • chunks_per_source: 1–3 (advanced only)
  • max_results: 0–20
  • topic: general | news | finance
  • time_range: day|week|month|year|d|w|m|y
  • start_date, end_date: YYYY-MM-DD
  • include_answer: false | true | basic | advanced
  • include_raw_content: false | true | markdown | text
  • include_images: boolean
  • include_image_descriptions: boolean
  • include_favicon: boolean
  • include_domains, exclude_domains: string arrays
  • country: country name (general topic only)
  • auto_parameters: boolean
  • include_usage: boolean

Expected response fields:

  • answer (if requested), results[] with title, url, content, score, raw_content (optional), favicon (optional)
  • response_time, usage, request_id

2) extract → POST /extract

Use for extracting content from specific URLs.

curl -sS -X POST "https://api.tavily.com/extract" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "urls": ["https://example.com/article"],
    "query": "<optional intent for reranking>",
    "chunks_per_source": 3,
    "extract_depth": "basic",
    "format": "markdown",
    "include_images": false,
    "include_favicon": false
  }'

Key parameters:

  • urls (required): array of URLs
  • query: rerank chunks by intent
  • chunks_per_source: 1–5 (only when query provided)
  • extract_depth: basic | advanced
  • format: markdown | text
  • timeout: 1–60 seconds
  • include_usage: boolean

Expected response fields:

  • results[] with url, raw_content, images, favicon
  • failed_results[], response_time, usage, request_id

3) map → POST /map

Use for generating a site map (URL discovery only).

curl -sS -X POST "https://api.tavily.com/map" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "url": "https://docs.tavily.com",
    "max_depth": 1,
    "max_breadth": 20,
    "limit": 50,
    "allow_external": true
  }'

Key parameters:

  • url (required)
  • instructions: natural language guidance (raises cost)
  • max_depth: 1–5
  • max_breadth: 1+
  • limit: 1+
  • select_paths, select_domains, exclude_paths, exclude_domains: arrays of regex strings
  • allow_external: boolean
  • timeout: 10–150 seconds
  • include_usage: boolean

Expected response fields:

  • base_url, results[] (list of URLs), response_time, usage, request_id

4) crawl → POST /crawl

Use for site traversal with built-in extraction.

curl -sS -X POST "https://api.tavily.com/crawl" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "url": "https://docs.tavily.com",
    "instructions": "Find all pages about the Python SDK",
    "max_depth": 1,
    "max_breadth": 20,
    "limit": 50,
    "extract_depth": "basic",
    "format": "markdown",
    "include_images": false
  }'

Key parameters:

  • url (required)
  • instructions: optional; raises cost and enables chunks_per_source
  • chunks_per_source: 1–5 (only with instructions)
  • max_depth, max_breadth, limit: same as map
  • extract_depth: basic | advanced
  • format: markdown | text
  • include_images, include_favicon, allow_external
  • timeout: 10–150 seconds
  • include_usage: boolean

Expected response fields:

  • base_url, results[] with url, raw_content, favicon
  • response_time, usage, request_id

Optional Research Workflow (Deep Investigation)

Use when a query needs multi-step analysis and citations.

create research task → POST /research

curl -sS -X POST "https://api.tavily.com/research" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{
    "input": "<research question>",
    "model": "auto",
    "stream": false,
    "citation_format": "numbered"
  }'

Expected response fields:

  • request_id, created_at, status (pending), input, model, response_time

get research status → GET /research/{request_id}

curl -sS -X GET "https://api.tavily.com/research/<request_id>" \
  -H "Authorization: Bearer $TAVILY_API_KEY"

Expected response fields:

  • status: completed
  • content: report text or structured object
  • sources[]: { title, url, favicon }

streaming research (SSE)

Set "stream": true in the POST body and use curl with -N to stream events:

curl -N -X POST "https://api.tavily.com/research" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TAVILY_API_KEY" \
  -d '{"input":"<question>","stream":true,"model":"pro"}'

Handle SSE events (tool calls, tool responses, content chunks, sources, done).

Usage Notes

  • Treat search, extract, map, and crawl as the primary endpoints for discovery and content retrieval.
  • Return structured results with URLs, titles, and summaries for easy downstream use.
  • Default to conservative parameters (search_depth: basic, max_results: 5) unless deeper recall is needed.
  • Reuse consistent request bodies across calls to keep results predictable.

Error Handling

  • If any request returns 401/403, prompt for or re-check TAVILY_API_KEY.
  • If timeouts occur, reduce max_depth/limit or use search_depth: basic.
  • If responses are too large, lower max_results or chunks_per_source.
how to use tavily

How to use tavily on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tavily
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/intellectronica/agent-skills --skill tavily

The skills CLI fetches tavily from GitHub repository intellectronica/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tavily

Reload or restart Cursor to activate tavily. Access the skill through slash commands (e.g., /tavily) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.672 reviews
  • Pratham Ware· Dec 28, 2024

    Registry listing for tavily matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aarav Ndlovu· Dec 28, 2024

    I recommend tavily for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mia Chawla· Dec 20, 2024

    tavily reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Lucas Torres· Dec 20, 2024

    Solid pick for teams standardizing on skills: tavily is focused, and the summary matches what you get after install.

  • Sophia Malhotra· Dec 16, 2024

    Registry listing for tavily matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Hana Martin· Dec 8, 2024

    Useful defaults in tavily — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Henry Agarwal· Nov 27, 2024

    tavily fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sophia Liu· Nov 27, 2024

    We added tavily from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sakshi Patil· Nov 19, 2024

    tavily reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • William Kapoor· Nov 19, 2024

    Keeps context tight: tavily is the kind of skill you can hand to a new teammate without a long onboarding doc.

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