sanjay3290/ai-skills

deep-research

Execute autonomous multi-step research using Google Gemini Deep Research Agent.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/sanjay3290/ai-skills --skill deep-research

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

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
  • GEMINIAPIKEY 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

MetricValue
Time2-10 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)

Verwandte Aufgaben