Agent Skills
› tavily-ai/skills
› tavily-research
tavily-research
GitHub基于Tavily CLI的深度AI研究技能，支持多源信息综合、对比分析及文献综述，生成带引用的结构化报告。适用于需要深度洞察的市场分析或复杂主题调研场景。
Trigger Scenarios
用户要求深度研究、详细报告或对比分析
需要进行市场趋势分析或多源信息综合
执行工作流中的最后一步研究阶段
Install
npx skills add tavily-ai/skills --skill tavily-research -g -y
SKILL.md
Frontmatter
{
"name": "tavily-research",
"description": "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",
"allowed-tools": "Bash(tvly *)"
}
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
--streamto see progress in real-time. - Use
--model profor complex comparisons or multi-faceted topics. - Use
--output-schemato get structured JSON output matching a custom schema. - For quick facts, use
tvly searchinstead — 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
Version History
- ea5e820 Current 2026-07-25 07:40
