Skills tavily-research

Name: tavily-research
Author: openclaw

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/abigale-cyber/content-system-tavily-research" ~/.claude/skills/openclaw-skills-tavily-research && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/abigale-cyber/content-system-tavily-research" ~/.openclaw/skills/openclaw-skills-tavily-research && rm -rf "$T"

manifest: skills/abigale-cyber/content-system-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 any command

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
```
--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
```