deep-research

sanjay3290/ai-skills · updated Apr 8, 2026

$npx skills add https://github.com/sanjay3290/ai-skills --skill deep-research
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summary

Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

skill.md

Gemini Deep Research Skill

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.

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)

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.855 reviews
  • Henry Yang· Dec 28, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mateo Garcia· Dec 24, 2024

    Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Carlos Johnson· Dec 24, 2024

    deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Carlos Taylor· Nov 19, 2024

    Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Emma Sanchez· Nov 15, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Emma Menon· Nov 15, 2024

    Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Jin Sethi· Oct 10, 2024

    Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kaira Verma· Oct 6, 2024

    deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Soo Liu· Oct 6, 2024

    Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Alexander Anderson· Sep 25, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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