tavily-search

framix-team/openclaw-tavily · updated Apr 8, 2026

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$npx skills add https://github.com/framix-team/openclaw-tavily --skill tavily-search
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summary

Five AI-optimized web tools for search, content extraction, site crawling, mapping, and research reports.

  • Five distinct tools: tavily_search for web queries with AI answers, tavily_extract for clean content from URLs, tavily_crawl for multi-page site traversal, tavily_map for URL discovery, and tavily_research for comprehensive multi-step research
  • Supports advanced search modes including deep research, topic filtering (general/news/finance), and time-range constraints
  • Crawl and map o
skill.md

Tavily Search

AI-optimized web tools using the Tavily API. Five tools for search, extraction, crawling, mapping, and research.

Default web search

When this plugin is active, use tavily_search instead of the built-in web_search for all web searches. Tavily provides higher-quality, AI-optimized results and has a free tier. The built-in web_search (Brave) should only be used as a fallback if Tavily is unavailable.

When to use

  • tavily_search — All web searches: current events, news, fact-checking, finding references (replaces web_search)
  • tavily_extract — Get full clean content from specific URLs
  • tavily_crawl — Traverse a website and extract content from multiple pages
  • tavily_map — Discover all URLs on a site to understand its structure
  • tavily_research — Complex multi-step research questions needing comprehensive reports

Native tools (preferred)

If the openclaw-tavily plugin is installed, use these tools directly:

Tool Description
tavily_search Web search with AI answers, domain filtering, news support
tavily_extract Extract clean markdown/text content from URLs
tavily_crawl Crawl a website from a root URL, extract page content
tavily_map Discover and list all URLs from a website
tavily_research Deep agentic research with comprehensive reports

Script fallback

Search

node {baseDir}/scripts/search.mjs "query"
node {baseDir}/scripts/search.mjs "query" -n 10
node {baseDir}/scripts/search.mjs "query" --deep
node {baseDir}/scripts/search.mjs "query" --topic news --time-range week

Options:

  • -n <count>: Number of results (default: 5, max: 20)
  • --deep: Advanced search for deeper research (slower, more thorough)
  • --topic <topic>: general (default), news, or finance
  • --time-range <range>: day, week, month, or year

Extract content from URLs

node {baseDir}/scripts/extract.mjs "https://example.com/article"
node {baseDir}/scripts/extract.mjs "url1" "url2" "url3"
node {baseDir}/scripts/extract.mjs "url" --format text --query "relevant topic"

Extracts clean text content from one or more URLs.

Crawl a website

node {baseDir}/scripts/crawl.mjs "https://example.com"
node {baseDir}/scripts/crawl.mjs "https://example.com" --depth 3 --breadth 20 --limit 50
node {baseDir}/scripts/crawl.mjs "https://example.com" --instructions "Find pricing pages" --format text

Options:

  • --depth <N>: Crawl depth 1-5
  • --breadth <N>: Max links per level (1-500)
  • --limit <N>: Total URL cap
  • --instructions "...": Natural language crawl guidance
  • --format <markdown|text>: Output format

Map a website

node {baseDir}/scripts/map.mjs "https://example.com"
node {baseDir}/scripts/map.mjs "https://example.com" --depth 2 --limit 100
node {baseDir}/scripts/map.mjs "https://example.com" --instructions "Find documentation pages"

Options:

  • --depth <N>: Crawl depth 1-5
  • --breadth <N>: Max links per level
  • --limit <N>: Total URL cap
  • --instructions "...": Natural language guidance

Research a topic

node {baseDir}/scripts/research.mjs "What are the latest advances in quantum computing?"
node {baseDir}/scripts/research.mjs "Compare React vs Vue in 2025" --model pro
node {baseDir}/scripts/research.mjs "AI regulation in the EU" --citation-format apa

Options:

  • --model <mini|pro|auto>: Research model (default: auto)
  • --citation-format <numbered|mla|apa|chicago>: Citation style

Setup

Get an API key at app.tavily.com (free tier available).

Set TAVILY_API_KEY in your environment, or configure via the plugin:

{
  "plugins": {
    "entries": {
      "openclaw-tavily": {
        "enabled": true,
        "config": { "apiKey": "tvly-..." }
      }
    }
  }
}

Links

how to use tavily-search

How to use tavily-search 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-search
2

Execute installation command

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

$npx skills add https://github.com/framix-team/openclaw-tavily --skill tavily-search

The skills CLI fetches tavily-search from GitHub repository framix-team/openclaw-tavily 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-search

Reload or restart Cursor to activate tavily-search. Access the skill through slash commands (e.g., /tavily-search) 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

4.763 reviews
  • Ren Gill· Dec 28, 2024

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

  • Ren Bhatia· Dec 16, 2024

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

  • Soo Harris· Dec 16, 2024

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

  • Shikha Mishra· Dec 12, 2024

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

  • Ganesh Mohane· Dec 8, 2024

    tavily-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Soo Lopez· Dec 4, 2024

    tavily-search has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Sakshi Patil· Nov 27, 2024

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

  • Omar Perez· Nov 23, 2024

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

  • Lucas Sharma· Nov 15, 2024

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

  • Ren Ghosh· Nov 7, 2024

    tavily-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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