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Tavily Research

  • 7 installs
  • 1 repo stars
  • Updated July 28, 2026
  • tavily-ai/tavily-cursor-plugin

This is a copy of tavily-research by tavily-ai - installs and ranking accrue to the original listing.

Helps with ai & agent building tasks during AI-assisted development.

About

tavily-research is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • tavily-research
  • AI & Agent Building
  • AI-coding skill

Tavily Research by the numbers

  • 7 all-time installs (skills.sh)
  • +2 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/tavily-ai/tavily-cursor-plugin --skill tavily-research

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Listed on Skillselion
Installs7
repo stars1
Last updatedJuly 28, 2026
Repositorytavily-ai/tavily-cursor-plugin

What it does

Helps with ai & agent building tasks during AI-assisted development.

Files

SKILL.mdMarkdownGitHub ↗

tavily research

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

Prerequisites

Requires the Tavily CLI. See tavily-cli for install and auth setup.

Quick install: curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login

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

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 output to 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:

# 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

  • tavily-search — quick web search for simple lookups
  • tavily-crawl — bulk extract from a site for your own analysis

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