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tavily-research▌

tavily-ai/skills · updated Apr 8, 2026

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$npx skills add https://github.com/tavily-ai/skills --skill tavily-research
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summary

Comprehensive AI-powered research with multi-source synthesis and citations.

  • ›Produces structured reports grounded in web sources, taking 30-120 seconds depending on model selection (mini for targeted queries, pro for complex comparisons)
  • ›Supports multiple output formats: markdown reports, JSON with custom schemas, and configurable citation styles (numbered, MLA, APA, Chicago)
  • ›Includes async workflow for long-running research via --no-wait , status , and poll commands, plus real-time
skill.md

tavily 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 --stream to see progress in real-time.
  • Use --model pro for complex comparisons or multi-faceted topics.
  • Use --output-schema to get structured JSON output matching a custom schema.
  • For quick facts, use tvly search instead — research is for deep synthesis.
  • Read from stdin: echo "query" | tvly research - --json

See also

how to use tavily-research

How to use tavily-research 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-research
2

Execute installation command

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

$npx skills add https://github.com/tavily-ai/skills --skill tavily-research

The skills CLI fetches tavily-research from GitHub repository tavily-ai/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-research

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

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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)
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general reviews

Ratings

4.5★★★★★48 reviews
  • ★★★★★Ganesh Mohane· Dec 24, 2024

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

  • ★★★★★Amina Bansal· Dec 24, 2024

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

  • ★★★★★Yuki Okafor· Dec 16, 2024

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

  • ★★★★★Amina Robinson· Dec 16, 2024

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

  • ★★★★★Sakshi Patil· Nov 23, 2024

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

  • ★★★★★Rahul Santra· Nov 15, 2024

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

  • ★★★★★Amina Choi· Nov 15, 2024

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

  • ★★★★★Ishan Abbas· Nov 15, 2024

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

  • ★★★★★Layla Bansal· Nov 7, 2024

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

  • ★★★★★Yusuf Liu· Nov 7, 2024

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

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