openlair / gemini-deep-research

Name: openlair/gemini-deep-research
Author: openlair

Perform deep, multi-source research using Google Gemini's Deep Research Agent. Use this skill whenever the user asks for comprehensive research, literature reviews, competitive analysis, market research, technology surveys, or any investigation that requires synthesizing information from many web sources. Also trigger when the user says "deep research", "research this thoroughly", "do a comprehensive study on", or wants a structured report with evidence gathered from across the web — even if they don't mention Gemini by name.

Version

SKILL.md

3.9 KB

Gemini Deep Research

Google Gemini's Deep Research Agent autonomously breaks down complex queries, searches the web systematically, and produces structured markdown reports with citations. It handles the kind of multi-source synthesis that would take a human hours of browsing.

Prerequisites

GEMINI_API_KEY environment variable must be set (obtain from Google AI Studio)
Python 3.8+ with the requests library installed
Requires a direct Gemini API key — OAuth tokens are not supported

How to Run the Script

The script is at scripts/deep_research.py relative to this skill's directory (i.e., the directory containing this SKILL.md). Resolve the full path from the skill's location before running.

python3 <this-skill-directory>/scripts/deep_research.py \
  --query "<research query>" \
  --stream \
  --output-dir ./reports

Key flags

Flag	Purpose	Default
`--query`	(required) The research question	—
`--stream`	Print progress updates while waiting	off
`--output-dir`	Where to save the report files	current dir
`--format`	Custom output structure (see example below)	free-form
`--file-search-store`	Gemini file-search store name	none
`--api-key`	Override `GEMINI_API_KEY` env var	env var

Before running

Check for GEMINI_API_KEY: Run echo $GEMINI_API_KEY to see if it's set. If empty, ask the user whether they'd like to provide a Gemini API key (they can get one from https://aistudio.google.com/apikey). If the user provides one, pass it via --api-key. If the user declines, do not use this skill — fall back to other research approaches and let the user know why.
Ensure requests is installed: python3 -c "import requests". If missing, install it: pip3 install requests.

Example commands

Basic research:

python3 <this-skill-directory>/scripts/deep_research.py \
  --query "Current state of quantum error correction techniques" \
  --stream --output-dir ./reports

Custom output format:

python3 <this-skill-directory>/scripts/deep_research.py \
  --query "Competitive landscape of EV batteries" \
  --format "1. Executive Summary\n2. Key Players (data table)\n3. Technology Comparison\n4. Supply Chain Risks" \
  --stream --output-dir ./reports

Output

The script produces two timestamped files in the output directory:

deep-research-YYYY-MM-DD-HH-MM-SS.md — the final markdown report
deep-research-YYYY-MM-DD-HH-MM-SS.json — full interaction metadata

The report is also printed to stdout so you can capture it directly.

Execution Notes

This is a long-running task — it typically takes 2–10 minutes depending on query complexity. Use --stream so the user can see progress.
Always run with a reasonable timeout (at least 600000ms / 10 minutes) when using the Bash tool.
After the script finishes, read and present the generated .md report to the user. Summarize key findings and point them to the full report file.

API Details

Endpoint: https://generativelanguage.googleapis.com/v1beta/interactions
Agent model: deep-research-pro-preview-12-2025
Auth: x-goog-api-key header