app-store-optimization/skills/deep-research/SKILL.md

name, description, source, risk

name	description	source	risk
deep-research	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.	https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research	safe

Gemini Deep Research Skill

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

When to Use This Skill

Use this skill when:

• Performing market analysis
• Conducting competitive landscaping
• Creating literature reviews
• Doing technical research
• Performing due diligence
• Need detailed, cited research 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)