deep-research
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
About this skill
The `deep-research` skill empowers AI agents to conduct sophisticated, autonomous research tasks. It orchestrates a multi-step process: first, it intelligently plans the research strategy; then, it performs comprehensive searches across various sources; subsequently, it critically reads and extracts relevant information; and finally, it synthesizes all gathered data into well-structured, comprehensive reports. This skill is ideal for generating detailed analyses on a wide range of topics, from market trends and competitive landscapes to technical reviews and due diligence reports, providing actionable insights with proper citation.
Best use case
Market analysis Competitive landscaping Literature reviews Technical research Due diligence Generating detailed, cited research reports
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
A comprehensive, well-structured, and cited research report synthesizing information from multiple sources on the specified topic.
Practical example
Example input
Conduct deep research on the emerging trends and key players in the global hydrogen energy market, focusing on technological advancements and investment opportunities.
Example output
```
Research Report: Emerging Trends and Key Players in the Global Hydrogen Energy Market
I. Executive Summary
II. Introduction to Hydrogen Energy
A. Types of Hydrogen Production
B. Current Market Size and Growth Projections
III. Emerging Trends
A. Green Hydrogen Production Scale-up
B. Advancements in Fuel Cell Technology
C. Policy and Regulatory Support
D. Infrastructure Development (Storage and Transport)
IV. Key Players and Innovators
A. Major Energy Companies (e.g., BP, Shell, TotalEnergies)
B. Specialized Hydrogen Technology Firms (e.g., Plug Power, ITM Power)
C. Startups and R&D Initiatives
V. Investment Opportunities
A. Production Facilities
B. Electrolyzer Manufacturing
C. Hydrogen Storage and Distribution
D. End-Use Applications (e.g., transport, industrial feedstock)
VI. Challenges and Outlook
A. Cost Competitiveness
B. Energy Efficiency
C. Safety Concerns
VII. Conclusion
VIII. References (cited sources)
Please let me know if you would like to delve deeper into any specific section or topic.
```When to use this skill
- Utilize this skill when you require in-depth, structured, and cited information on a specific topic. It's perfect for scenarios demanding a thorough understanding of a subject, such as identifying market opportunities, assessing competitor strategies, compiling academic overviews, exploring new technologies, or verifying facts for critical business decisions. This skill excels at producing high-quality, synthesized reports that save significant manual research time.
When not to use this skill
- Avoid this skill for tasks requiring real-time data that changes minute-by-minute (e.g., live stock prices), highly subjective opinions, creative writing assignments, or simple fact-checking that can be answered with a quick search. It is optimized for comprehensive, multi-source synthesis, not for immediate, single-point data retrieval or tasks where human intuition and creativity are paramount.
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 | Claude, Gemini | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | medium | N/A |
Frequently Asked Questions
What does this skill do?
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
Which AI agents support this skill?
This skill is designed for Claude, Gemini.
How difficult is it to install?
The installation complexity is rated as medium. You can find the installation instructions above.
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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