deep-research

name	deep-research
description	Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.
license	Apache-2.0
metadata	{"author":"sanjay3290","version":"2.0"}

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 (async)

python3 scripts/research.py --query "Research the history of Kubernetes"
# Returns interaction_id immediately

With structured output format

python3 scripts/research.py --query "Compare Python web frameworks" \
  --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"

Check status of running research

python3 scripts/research.py --status <interaction_id>
# Returns: {"status": "running|completed|failed", "result": "...", ...}

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

Execute step-by-step (do NOT write polling loops):

Step 1: Start research
→ python3 scripts/research.py --query "..." --json
→ Record the interaction_id from output

Step 2: Wait 30 seconds
→ sleep 30

Step 3: Check status
→ python3 scripts/research.py --status <interaction_id> --json

Step 4: Evaluate status:
→ If status == "completed": Output result to user
→ If status == "failed": Report error to user
→ If status == "running": Go back to Step 2

Exit Codes

• 0: Success
• 1: Error (API error, config issue)