How do I install the Deep Research skill?

Run npx skillmds@latest add zerone-agent/deep-research in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Deep Research safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Deep Research?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Deep Research free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Deep Research?

zerone-agent (@zerone-agent) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Deep Research by zerone-agent — Agent Skill

Name: Deep Research
Author: zerone-agent

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.

zerone-agent eb4f6f2 4 files · 22.8 KB Updated Sep 17, 2026

File contents

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:
```
export GEMINI_API_KEY=your-api-key-here
```
Or create a .env file in the skill directory.

Usage

Start a research task (async)

python3 scripts/research.py --query "Research the history of Kubernetes"
# Returns interaction_id immediately

With structured output format

python3 scripts/research.py --query "Compare Python web frameworks" \
  --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"

Check status of running research

python3 scripts/research.py --status <interaction_id>
# Returns: {"status": "running|completed|failed", "result": "...", ...}

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

Execute step-by-step (do NOT write polling loops):

Step 1: Start research
→ python3 scripts/research.py --query "..." --json
→ Record the interaction_id from output

Step 2: Wait 30 seconds
→ sleep 30

Step 3: Check status
→ python3 scripts/research.py --status <interaction_id> --json

Step 4: Evaluate status:
→ If status == "completed": Output result to user
→ If status == "failed": Report error to user
→ If status == "running": Go back to Step 2

Exit Codes

0: Success
1: Error (API error, config issue)

---name: deep-researchdescription: 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---# Gemini Deep Research SkillRun 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## Setup1. 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 (async)```bashpython3 scripts/research.py --query "Research the history of Kubernetes"# Returns interaction_id immediately```### With structured output format```bashpython3 scripts/research.py --query "Compare Python web frameworks" \ --format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"```### Check status of running research```bashpython3 scripts/research.py --status <interaction_id># Returns: {"status": "running|completed|failed", "result": "...", ...}```### Continue from previous research```bashpython3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>```### List recent research```bashpython3 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**Execute step-by-step (do NOT write polling loops):**```Step 1: Start research→ python3 scripts/research.py --query "..." --json→ Record the interaction_id from outputStep 2: Wait 30 seconds→ sleep 30Step 3: Check status→ python3 scripts/research.py --status <interaction_id> --jsonStep 4: Evaluate status:→ If status == "completed": Output result to user→ If status == "failed": Report error to user→ If status == "running": Go back to Step 2```## Exit Codes- **0**: Success- **1**: Error (API error, config issue)

Frequently asked questions