| 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 ... |
| risk | safe |
| source | https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research |
| date_added | 2026-02-27 |
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
- Get a Gemini API key from Google AI Studio
- Set the environment variable:
export GEMINI_API_KEY=your-api-key-here
Or create a .env file in the skill directory.
Usage
Start a research task
python3 scripts/research.py --query "Research the history of Kubernetes"
With structured output format
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
Stream progress in real-time
python3 scripts/research.py --query "Analyze EV battery market" --stream
Start without waiting
python3 scripts/research.py --query "Research topic" --no-wait
Check status of running research
python3 scripts/research.py --status <interaction_id>
Wait for completion
python3 scripts/research.py --wait <interaction_id>
Continue from previous research
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
List recent research
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
- User requests research → Run
--query "..."
- Inform user of estimated time (2-10 minutes)
- Monitor with
--stream or poll with --status
- Return formatted results
- Use
--continue for follow-up questions
Exit Codes
- 0: Success
- 1: Error (API error, config issue, timeout)
- 130: Cancelled by user (Ctrl+C)
