Back to Skills

Deep Research

Skills ai-research 186 Scanned by NVIDIA SkillSpector

Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

Install Command
npx claude-code-templates@latest --skill ai-research/deep-research
View on GitHub
claude-code — deep-research

Scanned by NVIDIA SkillSpector

Automated static analysis (no LLM) · v2.12.0 · 2026-10-24

Partial analysis: some checks could not run on this component.

0 observations
scanner score 0/100

SkillSpector looks for patterns such as shell commands, credential access, network calls or unpinned dependencies. An observation is something worth reading in the files before you install, not a confirmed vulnerability. Components that run code locally, like hooks, MCP servers and security tooling, naturally show more of them.

No observations.

Learn more about NVIDIA SkillSpector.

Content

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
  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)

Stack Builder

0 components

Your stack is empty

Browse components and click the + button to add them to your stack for easy installation.