gemini-deep-research

Files (2)

• SKILL.md
• deep_research.py · scripts/

SKILL.md (first lines)

# gemini-deep-research

## Canonical Summary

Perform complex, long-running research tasks using Gemini Deep Research Agent. Use when: asked to research topics requiring multi-source synthesis, competitive analysis, market research, literature review, or comprehensive technical invest...

## Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

## Resource Use Rules

- Treat `scripts/` as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.

## Execution Contract

- Resolve every relative path from this skill directory first.
- Prefer inspection before mutation when invoking bundled scripts.
- If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
- Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

## Upstream Instructions

# Gemini Deep Research

Use Google Gemini's Deep Research Agent to perform complex, long-running context gathering and synthesis tasks. The agent autonomously breaks down your query, searches the web, and synthesizes findings into a comprehensive report.

## Prerequisites

- `GEMINI_API_KEY` environment variable (obtain from [Google AI Studio](https://aistudio.google.com/apikey))
- Python 3.8+ with `requests` library
- **Note**: Requires a direct Gemini API key — OAuth tokens are not supported.

## When to Use

- Comprehensive literature or market research
- Competitive landscape analysis
- Technical deep dives requiring multi-source synthesis
- Any research task that benefits from systematic web search
- When you need a structured report with evidence from multiple sources

## How It Works

The Deep Research agent:
1. Breaks down complex queries into sub-questions
2. Searches the web systematically for each sub-question
3. Synthesizes findings into a comprehensive markdown report
4. Provides streaming progress updates during execution

## Usage

### Basic Research

```bash
scripts/deep_research.py --query "Research the current state of quantum error correction" --stream
```

### Custom Output Format

```bash
scripts/deep_research.py --query "Competitive landscape of EV batteries" \
  --format "1. Executive Summary\n2. Key Players (data table)\n3. Technology Comparison\n4. Supply Chain Risks"
```

### With File Search (optional)

```bash
scripts/deep_research.py --query "Compare our fiscal year report against current public web news" \
  --file-search-store "fileSearchStores/my-store-name"
```

### Specify Output Directory

```bash
scripts/deep_research.py --query "Your research topic" --output-dir ./reports --stream
```

## Output

Results are saved as timestamped files in the output directory:
- `deep-research-YYYY-MM-DD-HH-MM-SS.md` — Final report in markdown
- `deep-research-YYYY-MM-DD-HH-MM-SS.json` — Full interaction metadata

The report is also printed to stdout for immediate use.

## API Details

- **Endpoint**: `https://generativelanguage.googleapis.com/v1beta/interactions`
- **Agent model**: `deep-research-pro-preview-12-2025`
- **Auth**: `x-goog-api-key` header

## Limitations

- Long-running tasks (minutes to tens of minutes depending on complexity)
- May incur API costs depending on your Google AI quota
- Results depend on web content available at query time

From LigphiDonk/Oh-my--paper, MIT.

More from LigphiDonk/Oh-my--paper