Deep Research
Execute autonomous multi-step research and generate detailed, cited reports.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/deep-research-sanjay3290/— the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
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.
What this skill does
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
- Get a Gemini API key from Google AI Studio
- Set the environment variable:
Or create a
export GEMINI_API_KEY=your-api-key-here.envfile in the skill directory.
Usage
Start a research task
python3 scripts/research.py --query "Research the history of Kubernetes"
With structured output format
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
Stream progress in real-time
python3 scripts/research.py --query "Analyze EV battery market" --stream
Start without waiting
python3 scripts/research.py --query "Research topic" --no-wait
Check status of running research
python3 scripts/research.py --status <interaction_id>
Wait for completion
python3 scripts/research.py --wait <interaction_id>
Continue from previous research
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
List recent research
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
- User requests research → Run
--query "..." - Inform user of estimated time (2-10 minutes)
- Monitor with
--streamor poll with--status - Return formatted results
- Use
--continuefor follow-up questions
Exit Codes
- 0: Success
- 1: Error (API error, config issue, timeout)
- 130: Cancelled by user (Ctrl+C)
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