- 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-hereOr 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\
2. Comparison Table\
3. Recommendations"Stream progress in real-time
python3 scripts/research.py --query "Analyze EV battery market" --streamStart without waiting
python3 scripts/research.py --query "Research topic" --no-waitCheck 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 --listOutput 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
--streamor poll with--status - Return formatted results
- Use
--continuefor follow-up questions
Exit Codes
- 0: Success
- 1: Error (API error, config issue, timeout)
- 130: Cancelled by user (Ctrl+C)