gemini-deep-research

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

1. Get a Gemini API key from Google AI Studio
2. 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

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)

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.