Comprehensive AI-powered research with multi-source synthesis and citations.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontavily-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches tavily-research from tavily-ai/skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tavily-research. Access via /tavily-research in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.
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.
# 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
| 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 | 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.
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
--stream to see progress in real-time.--model pro for complex comparisons or multi-faceted topics.--output-schema to get structured JSON output matching a custom schema.tvly search instead — research is for deep synthesis.echo "query" | tvly research - --jsonMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
tavily-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend tavily-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for tavily-research matched our evaluation — installs cleanly and behaves as described in the markdown.
tavily-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
tavily-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend tavily-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tavily-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
tavily-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in tavily-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tavily-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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