research
Comprehensive research grounded in web data with explicit citations. Use when you need multi-source synthesis—comparisons, current events, market analysis, detailed reports.
Savant verdict: Review advised
Security analysis or source-hub review identified patterns requiring attention.
Live evaluation
Not evaluated yet. Workspaces can request a live evaluation.
Security assessment
Risk score
47/100
Recommendation
CAUTION
Severity
MEDIUM
Savant decision
Review advised
The assessment recommends caution with a risk score of 20/100 or more; review its findings before use. Static security analysis completed.
4 patterns found
- Agent Snooping: ~/.claude/settings.jsonhigh · SKILL.md:19 · Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.
- MCP Least Privilege: Skill declares no tool scope ('permissions' or 'allowed-tools') but code capabilities were detected: shell.medium · SKILL.md:1 · Without declared permissions the skill's intent is opaque and cannot be validated.
- MCP Rug Pull: npx -y mcp-remotemedium · scripts/research.sh:101 · npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
- Data Exfiltration: curl -s --request POST \ --url "https://mcp.tavily.com/mcp" \ --header "Authorization: Bearer $TAVILY_API_KEY" \ --header 'Content-Type: application/json' \ --header 'Accept: applicatimedium · scripts/research.sh:160 · Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
Structure
- Ships 1 executable scriptscripts/research.sh. Review what they do before enabling the skill for agents with tool access.
- No license declaredConfirm you may reuse this skill before importing it into your repository.
- The source hub's security checks flagged this skillGen Agent Trust Hub: warn (This research skill uses the Tavily API to perform web searches. It includes an automated authentication flow that uses npx to download and execute remote code and scans local directories for stored credentials.); Snyk: warn (Risk: MEDIUM · No issues)
SKILL.md
---
name: research
description: "Comprehensive research grounded in web data with explicit citations. Use when you need multi-source synthesis—comparisons, current events, market analysis, detailed reports. "
---
# Research Skill
Conduct comprehensive research on any topic with automatic source gathering, analysis, and response generation with citations.
## Authentication
The script uses OAuth via the Tavily MCP server. **No manual setup required** - on first run, it will:
1. Check for existing tokens in `~/.mcp-auth/`
2. If none found, automatically open your browser for OAuth authentication
> **Note:** You must have an existing Tavily account. The OAuth flow only supports login — account creation is not available through this flow. [Sign up at tavily.com](https://tavily.com) first if you don't have an account.
### Alternative: API Key
If you prefer using an API key, get one at https://tavily.com and add to `~/.claude/settings.json`:
```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
```bash
./scripts/research.sh '<json>' [output_file]
```
**Examples:**
```bash
# 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
# Quick targeted research
./scripts/research.sh '{"input": "climate change impacts", "model": "mini"}'
```
## Parameters
| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `input` | string | Required | Research topic or question |
| `model` | string | `"mini"` | Model: `mini`, `pro`, `auto` |
## 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 |
## Examples
### Quick Overview
```bash
./scripts/research.sh '{"input": "What is retrieval augmented generation?", "model": "mini"}'
```
### Technical Comparison
```bash
./scripts/research.sh '{"input": "LangGraph vs CrewAI for multi-agent systems", "model": "pro"}'
```
### Market Research
```bash
./scripts/research.sh '{"input": "Fintech startup landscape 2025", "model": "pro"}' fintech-report.md
```