research – OpenClaw Skill

---

name: research description: "Get AI-synthesized research on any topic with citations, directly in your terminal. Supports structured JSON output for pipelines. Use when you need comprehensive research grounded in web data without writing code."

Research Skill

Conduct comprehensive research on any topic with automatic source gathering, analysis, and response generation with citations.

Prerequisites

Tavily API Key Required - Get your key at https://tavily.com

Add to ~/.claude/settings.json:

{
  "env": {
    "TAVILY_API_KEY": "tvly-your-api-key-here"
  }
}

Quick Start

Tip: Research can take 30-120 seconds. Press Ctrl+B to run in the background.

Using the Script

./scripts/research.sh '<json>' [output_file]

Examples:

# Basic research
./scripts/research.sh '{"input": "quantum computing trends"}'

# With pro model for comprehensive analysis
./scripts/research.sh '{"input": "AI agents comparison", "model": "pro"}'

# Save to file
./scripts/research.sh '{"input": "market analysis for EVs", "model": "pro"}' ./ev-report.md

# With custom citation format
./scripts/research.sh '{"input": "climate change impacts", "model": "mini", "citation_format": "apa"}'

# With structured output schema
./scripts/research.sh '{"input": "fintech startups 2025", "model": "pro", "output_schema": {"properties": {"summary": {"type": "string"}, "companies": {"type": "array", "items": {"type": "string"}}}, "required": ["summary"]}}'

Basic Research

curl --request POST \
  --url https://api.tavily.com/research \
  --header "Authorization: Bearer $TAVILY_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "Latest developments in quantum computing",
    "model": "mini",
    "stream": false,
    "citation_format": "numbered"
  }'

Note: Streaming is disabled for token management. The call waits until research completes and returns clean JSON.

With Custom Schema

curl --request POST \
  --url https://api.tavily.com/research \
  --header "Authorization: Bearer $TAVILY_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "Electric vehicle market analysis",
    "model": "pro",
    "stream": false,
    "citation_format": "numbered",
    "output_schema": {
      "properties": {
        "market_overview": {
          "type": "string",
          "description": "2-3 sentence overview of the market"
        },
        "key_players": {
          "type": "array",
          "description": "Major companies in this market",
          "items": {
            "type": "object",
            "properties": {
              "name": {"type": "string", "description": "Company name"},
              "market_share": {"type": "string", "description": "Approximate market share"}
            },
            "required": ["name"]
          }
        }
      },
      "required": ["market_overview", "key_players"]
    }
  }'

API Reference

Endpoint

POST https://api.tavily.com/research

Headers

Header	Value
Authorization	Bearer <TAVILY_API_KEY>
Content-Type	application/json

Request Body

Field	Type	Default	Description
input	string	Required	Research topic or question
model	string	"mini"	Model: mini, pro, auto
stream	boolean	false	Streaming disabled for token management
output_schema	object	null	JSON schema for structured output
citation_format	string	"numbered"	Citation format: numbered, mla, apa, chicago

Response Format (JSON)

With stream: false, the response is clean JSON:

{
  "content": "# Research Results\n\n...",
  "sources": [{"url": "https://...", "title": "Source Title"}],
  "response_time": 45.2
}

Model Selection

Rule of thumb: "what does X do?" -> mini. "X vs Y vs Z" or "best way to..." -> pro.

Model	Use Case	Speed
mini	Single topic, targeted research	~30s
pro	Comprehensive multi-angle analysis	~60-120s
auto	API chooses based on complexity	Varies

Schema Usage

Schemas make output structured and predictable. Every property MUST include both type and description.

{
  "properties": {
    "summary": {
      "type": "string",
      "description": "2-3 sentence executive summary"
    },
    "key_points": {
      "type": "array",
      "description": "Main takeaways",
      "items": {"type": "string"}
    }
  },
  "required": ["summary", "key_points"]
}

Examples

Market Research

curl --request POST \
  --url https://api.tavily.com/research \
  --header "Authorization: Bearer $TAVILY_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "Fintech startup landscape 2025",
    "model": "pro",
    "stream": false,
    "citation_format": "numbered",
    "output_schema": {
      "properties": {
        "market_overview": {"type": "string", "description": "Executive summary of fintech market"},
        "top_startups": {
          "type": "array",
          "description": "Notable fintech startups",
          "items": {
            "type": "object",
            "properties": {
              "name": {"type": "string", "description": "Startup name"},
              "focus": {"type": "string", "description": "Primary business focus"},
              "funding": {"type": "string", "description": "Total funding raised"}
            },
            "required": ["name", "focus"]
          }
        },
        "trends": {"type": "array", "description": "Key market trends", "items": {"type": "string"}}
      },
      "required": ["market_overview", "top_startups"]
    }
  }'

Technical Comparison

curl --request POST \
  --url https://api.tavily.com/research \
  --header "Authorization: Bearer $TAVILY_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "LangGraph vs CrewAI for multi-agent systems",
    "model": "pro",
    "stream": false,
    "citation_format": "mla"
  }'

Quick Overview

curl --request POST \
  --url https://api.tavily.com/research \
  --header "Authorization: Bearer $TAVILY_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "input": "What is retrieval augmented generation?",
    "model": "mini",
    "stream": false
  }'