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Gemini Deep Research

Research
gemini-deep-research

Gemini Deep Research is an Agent Skill for research published in the sickn33/agentic-awesome-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/sickn33/agentic-awesome-skills/blob/HEAD/skills/gemini-deep-research/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/gemini-deep-research, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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

MetricValue
Time2-10 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.