Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/sanjay3290/ai-skills --skill deep-researchAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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
- Get a Gemini API key from Google AI Studio
- Set the environment variable:
bash
export GEMINI_API_KEY=your-api-key-hereOr 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" --streamStart without waiting
bash
python3 scripts/research.py --query "Research topic" --no-waitCheck 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 --listOutput 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
--streamor poll with--status - Return formatted results
- Use
--continuefor follow-up questions
Exit Codes
- 0: Success
- 1: Error (API error, config issue, timeout)
- 130: Cancelled by user (Ctrl+C)
