Gemini deep research

Skill sickn33/agentic-awesome-skills/skills/gemini-deep-research

AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 1,987+ agentic skills. Includes CLI, local MCP, catalog, plugins, and Workbench.

Install

npx -y skills add sickn33/agentic-awesome-skills --skill gemini-deep-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Run autonomous multi-step research with Google's Gemini Deep Research Agent: kick off a query, poll progress, and collect a cited report for market analysis or literature reviews.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.1 KB, as published. Nobody here has run it

Gemini Deep Research Skill

When to Use

Use when a question needs autonomous multi-step research with cited sources (market analysis, literature reviews, competitive scans)
Use when you want to start a Gemini Deep Research run, poll its progress, and collect the final report
Use when a quick web search is not enough and a structured, source-grounded report is required

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.

Safety Gate

Before starting a research job, show the user the exact query, the fact that it will be sent to Google's Gemini service, the expected cost range, and the output destination. Start a job only after explicit approval. Do not include private workspace material, credentials, personal data, or confidential customer information in a query.

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 --stream or poll with --status
Return formatted results
Use --continue for follow-up questions

Exit Codes

0: Success
1: Error (API error, config issue, timeout)
130: Cancelled by user (Ctrl+C)

Limitations

Each research job is a paid, third-party API request; costs and availability can change, and the listed estimate is not a spending authorization.
Reports may contain incomplete, stale, or incorrect citations. Verify consequential claims against primary sources.
This skill cannot guarantee that a prompt is safe to disclose; redact proprietary or personal material before requesting user approval.
An API key must remain local and must never be committed, printed, or sent in a query.