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deep-research

deep-research

github.com/w95Productivity28538 installsUpdated 35w ago
Repository

About deep-research

deep-research is a Python command-line skill for running autonomous research with Gemini. It plans and conducts searches, reads sources, and synthesizes reports, with options for progress tracking and structured output.

Key features

  1. 01Run research tasks through Gemini
  2. 02Stream progress or check task status
  3. 03Continue previous research
  4. 04Produce Markdown, JSON, or raw output
  5. 05Set a custom report format

Use cases

  1. 01Analyze markets and competitors
  2. 02Review technical literature
  3. 03Conduct due diligence research
  4. 04Compare frameworks or technologies

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/w95/awesome-claude-corporate-skills --skill deep-research

After installing, type/deep-researchin Claude Code to confirm it loaded.

Details

From the project README

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

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)

Comments

Markdown supported