aa71e76eb9
- Add date_added to all 950+ skills for complete tracking - Update version to 6.5.0 in package.json and README - Regenerate all indexes and catalog - Sync all generated files Features from merged PR #150: - Stars/Upvotes system for community-driven discovery - Auto-update mechanism via START_APP.bat - Interactive Prompt Builder - Date tracking badges - Smart auto-categorization All skills validated and indexed. Made-with: Cursor
116 lines
2.9 KiB
Markdown
116 lines
2.9 KiB
Markdown
---
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name: deep-research
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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 ..."
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risk: safe
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source: "https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research"
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date_added: "2026-02-27"
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---
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# Gemini Deep Research Skill
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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
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## When to Use This Skill
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Use this skill when:
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- Performing market analysis
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- Conducting competitive landscaping
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- Creating literature reviews
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- Doing technical research
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- Performing due diligence
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- Need detailed, cited research reports
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## Requirements
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- Python 3.8+
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- httpx: `pip install -r requirements.txt`
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- GEMINI_API_KEY environment variable
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## Setup
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1. Get a Gemini API key from [Google AI Studio](https://aistudio.google.com/)
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2. Set the environment variable:
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```bash
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export GEMINI_API_KEY=your-api-key-here
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```
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Or create a `.env` file in the skill directory.
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## Usage
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### Start a research task
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```bash
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python3 scripts/research.py --query "Research the history of Kubernetes"
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```
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### With structured output format
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```bash
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python3 scripts/research.py --query "Compare Python web frameworks" \
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--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
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```
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### Stream progress in real-time
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```bash
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python3 scripts/research.py --query "Analyze EV battery market" --stream
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```
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### Start without waiting
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```bash
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python3 scripts/research.py --query "Research topic" --no-wait
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```
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### Check status of running research
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```bash
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python3 scripts/research.py --status <interaction_id>
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```
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### Wait for completion
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```bash
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python3 scripts/research.py --wait <interaction_id>
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```
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### Continue from previous research
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```bash
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python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
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```
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### List recent research
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```bash
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python3 scripts/research.py --list
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```
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## Output Formats
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- **Default**: Human-readable markdown report
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- **JSON** (`--json`): Structured data for programmatic use
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- **Raw** (`--raw`): Unprocessed API response
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## Cost & Time
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| Metric | Value |
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|--------|-------|
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| Time | 2-10 minutes per task |
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| Cost | $2-5 per task (varies by complexity) |
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| Token usage | ~250k-900k input, ~60k-80k output |
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## Best Use Cases
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- Market analysis and competitive landscaping
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- Technical literature reviews
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- Due diligence research
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- Historical research and timelines
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- Comparative analysis (frameworks, products, technologies)
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## Workflow
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1. User requests research → Run `--query "..."`
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2. Inform user of estimated time (2-10 minutes)
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3. Monitor with `--stream` or poll with `--status`
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4. Return formatted results
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5. Use `--continue` for follow-up questions
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## Exit Codes
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- **0**: Success
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- **1**: Error (API error, config issue, timeout)
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- **130**: Cancelled by user (Ctrl+C)
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