annkalyuzhnaya/agent_practice/tavily-research

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Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says \"research\", \"investigate\", \"analyze in depth\", \"compare X vs Y\", \"what does the market look like for\", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.\n

Install

npx vskill@latest install annkalyuzhnaya/agent_practice/tavily-research

SKILL.md

tavily research

AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.

Before running

Research requires authentication. Run the requested command directly when tvly is already authenticated; do not add a status check to every invocation.

If tvly is missing, follow the tavily-cli setup. If an installed CLI reports an authentication error, use tvly login for authentication only, or tvly init --skip-skills when guided verification is also useful. Browser-based OAuth is preferred when an interactive user can complete it. --no-browser prints the sign-in link instead of opening it, but still waits for a localhost callback. In an unattended agent or CI environment, leave authentication to the user or use a securely provided TAVILY_API_KEY. Do not start a second login immediately after guided setup has completed.

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

bash
# 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.json
# JSON output for agents
tvly research "quantum computing breakthroughs" --json

Options

OptionDescription
--modelmini, pro, or auto (default)
--streamStream results in real-time
--no-waitReturn request_id immediately (async)
--output-schemaPath to JSON schema for structured output
--citation-formatnumbered, mla, apa, chicago
--poll-intervalSeconds between checks (default: 10)
--timeoutMax wait seconds (default: 600)
-o, --outputSave the JSON response to a file
--jsonStructured JSON output

Model selection

ModelUse forSpeed
miniSingle-topic, targeted research~30s
proComprehensive multi-angle analysis~60-120s
autoAPI chooses based on complexityVaries

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:

bash
# 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

Security

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Source

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