tavily▌
intellectronica/agent-skills · updated Apr 8, 2026
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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.
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_KEYin the environment. - If
TAVILY_API_KEYis 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 textsearch_depth:basic|advanced|fast|ultra-fastchunks_per_source: 1–3 (advanced only)max_results: 0–20topic:general|news|financetime_range:day|week|month|year|d|w|m|ystart_date,end_date:YYYY-MM-DDinclude_answer:false|true|basic|advancedinclude_raw_content:false|true|markdown|textinclude_images: booleaninclude_image_descriptions: booleaninclude_favicon: booleaninclude_domains,exclude_domains: string arrayscountry: country name (general topic only)auto_parameters: booleaninclude_usage: boolean
Expected response fields:
answer(if requested),results[]withtitle,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 URLsquery: rerank chunks by intentchunks_per_source: 1–5 (only whenqueryprovided)extract_depth:basic|advancedformat:markdown|texttimeout: 1–60 secondsinclude_usage: boolean
Expected response fields:
results[]withurl,raw_content,images,faviconfailed_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: 1–5max_breadth: 1+limit: 1+select_paths,select_domains,exclude_paths,exclude_domains: arrays of regex stringsallow_external: booleantimeout: 10–150 secondsinclude_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 enableschunks_per_sourcechunks_per_source: 1–5 (only withinstructions)max_depth,max_breadth,limit: same as mapextract_depth:basic|advancedformat:markdown|textinclude_images,include_favicon,allow_externaltimeout: 10–150 secondsinclude_usage: boolean
Expected response fields:
base_url,results[]withurl,raw_content,faviconresponse_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:completedcontent: report text or structured objectsources[]:{ 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, andcrawlas 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/limitor usesearch_depth: basic. - If responses are too large, lower
max_resultsorchunks_per_source.
How to use tavily on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tavily from GitHub repository intellectronica/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★72 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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