deep-research
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
Best use case
deep-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
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?
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
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. ## 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](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 ```bash python3 scripts/research.py --query "Research the history of Kubernetes" ``` ### With structured output format ```bash python3 scripts/research.py --query "Compare Python web frameworks" \ --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations" ``` ### Stream progress in real-time ```bash python3 scripts/research.py --query "Analyze EV battery market" --stream ``` ### Start without waiting ```bash python3 scripts/research.py --query "Research topic" --no-wait ``` ### Check status of running research ```bash python3 scripts/research.py --status <interaction_id> ``` ### Wait for completion ```bash python3 scripts/research.py --wait <interaction_id> ``` ### 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 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)
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