| name | gemini-deep-research |
| description | Run autonomous multi-step research with Google's Gemini Deep Research Agent: kick off a query, poll progress, and collect a cited report for market analysis or literature reviews. |
Gemini Deep Research Skill
When to Use
- Use when a question needs autonomous multi-step research with cited sources (market analysis, literature reviews, competitive scans)
- Use when you want to start a Gemini Deep Research run, poll its progress, and collect the final report
- Use when a quick web search is not enough and a structured, source-grounded report is required
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive 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.
Safety Gate
Before starting a research job, show the user the exact query, the fact that it will be sent
to Google's Gemini service, the expected cost range, and the output destination. Start a job
only after explicit approval. Do not include private workspace material, credentials, personal
data, or confidential customer information in a query.
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)
Limitations
- Each research job is a paid, third-party API request; costs and availability can change, and
the listed estimate is not a spending authorization.
- Reports may contain incomplete, stale, or incorrect citations. Verify consequential claims
against primary sources.
- This skill cannot guarantee that a prompt is safe to disclose; redact proprietary or personal
material before requesting user approval.
- An API key must remain local and must never be committed, printed, or sent in a query.
