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.
Best use case
deep-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using deep-research should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deep-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deep-research Compares
| Feature / Agent | deep-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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](https://aistudio.google.com/)
2. Set the environment variable:
```bash
export GEMINI_API_KEY=your-api-key-here
```
Or create a `.env` file in the skill directory.
## Usage
### Start a research task (async)
```bash
python3 scripts/research.py --query "Research the history of Kubernetes"
# Returns interaction_id immediately
```
### With structured output format
```bash
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
```
### Check status of running research
```bash
python3 scripts/research.py --status <interaction_id>
# Returns: {"status": "running|completed|failed", "result": "...", ...}
```
### Continue from previous research
```bash
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
```
### List recent research
```bash
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)Related Skills
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