sickn33/agentic-awesome-skills

gemini-deep-research

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.

First seen Jul 24, 2026

Installation

$ npx skills add sickn33/agentic-awesome-skills --skill gemini-deep-research

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More details

Agent compatibility

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Repository health

Stars 46.2K
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

LicenseApache-2.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,198 B
  • docs SUMMARY.md 207 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 6 installs

SKILL.md

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
  • GEMINIAPIKEY environment variable

Setup

  1. Get a Gemini API key from Google AI Studio
  2. Set the environment variable:

``bash export GEMINIAPIKEY=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.