/deep-research

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.

BOOST

sanjay3290@sanjay3290

GitHub

From plugin

ai-skills

43724 skills

Install

$ npx -y skills add sanjay3290/ai-skills --skill deep-research --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/deep-research

Context preview

The summary Claude sees to decide when to auto-load this skill.

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.

SKILL.md

deep-research.SKILL.md

name: deep-research
description: "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."
license: Apache-2.0
metadata:
  author: sanjay3290
  version: "1.0"

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

1. Get a Gemini API key from [Google AI Studio](https://aistudio.google.com/) 2. Set the environment variable:

   export GEMINI_API_KEY=your-api-key-here

Or create a `.env` file 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

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