Files
sck_0 dde0467e42 feat: add 61 new skills from VoltAgent repository
- 27 official team skills (Sentry, Trail of Bits, Expo, Hugging Face, etc.)
- 34 community skills including context engineering suite
- All skills validated and compliant with V4 quality bar
- Complete source attribution maintained

Skills added:
- Official: commit, create-pr, find-bugs, iterate-pr, culture-index, fix-review, sharp-edges, expo-deployment, upgrading-expo, hugging-face-cli, hugging-face-jobs, vercel-deploy-claimable, design-md, using-neon, n8n-*, swiftui-expert-skill, fal-*, deep-research, imagen, readme, screenshots
- Community: frontend-slides, linear-claude-skill, skill-rails-upgrade, context-*, multi-agent-patterns, tool-design, evaluation, memory-systems, terraform-skill, and more
2026-01-30 09:15:26 +01:00

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2.9 KiB
Markdown

---
name: deep-research
description: "Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task."
source: "https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research"
risk: safe
---
# Gemini Deep Research Skill
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
## When to Use This Skill
Use this skill when:
- Performing market analysis
- Conducting competitive landscaping
- Creating literature reviews
- Doing technical research
- Performing due diligence
- Need detailed, cited research reports
## Requirements
- Python 3.8+
- httpx: `pip install -r requirements.txt`
- GEMINI_API_KEY environment variable
## Setup
1. Get a Gemini API key from [Google AI Studio](https://aistudio.google.com/)
2. Set the environment variable:
```bash
export GEMINI_API_KEY=your-api-key-here
```
Or create a `.env` file in the skill directory.
## Usage
### Start a research task
```bash
python3 scripts/research.py --query "Research the history of Kubernetes"
```
### With structured output format
```bash
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
```
### Stream progress in real-time
```bash
python3 scripts/research.py --query "Analyze EV battery market" --stream
```
### Start without waiting
```bash
python3 scripts/research.py --query "Research topic" --no-wait
```
### Check status of running research
```bash
python3 scripts/research.py --status <interaction_id>
```
### Wait for completion
```bash
python3 scripts/research.py --wait <interaction_id>
```
### Continue from previous research
```bash
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
```
### List recent research
```bash
python3 scripts/research.py --list
```
## Output Formats
- **Default**: Human-readable markdown report
- **JSON** (`--json`): Structured data for programmatic use
- **Raw** (`--raw`): Unprocessed API response
## Cost & Time
| Metric | Value |
|--------|-------|
| Time | 2-10 minutes per task |
| Cost | $2-5 per task (varies by complexity) |
| Token usage | ~250k-900k input, ~60k-80k output |
## Best Use Cases
- Market analysis and competitive landscaping
- Technical literature reviews
- Due diligence research
- Historical research and timelines
- Comparative analysis (frameworks, products, technologies)
## Workflow
1. User requests research → Run `--query "..."`
2. Inform user of estimated time (2-10 minutes)
3. Monitor with `--stream` or poll with `--status`
4. Return formatted results
5. Use `--continue` for follow-up questions
## Exit Codes
- **0**: Success
- **1**: Error (API error, config issue, timeout)
- **130**: Cancelled by user (Ctrl+C)