deployer-training

name	deployer-training
description	Generate comprehensive deployer and developer training documentation by scanning a codebase. Use when user asks for onboarding guides, developer documentation, system overviews, or needs to understand a new codebase. Triggers on phrases like "create training materials", "generate developer guide", "explain this codebase", "onboarding documentation", or "deployer manual". Use when this capability is needed.
metadata	{"author":"dtmc-marketplace"}

Deployer Training Materials Generator

Generate comprehensive, production-quality training documentation by scanning and analyzing a codebase using Gemini AI.

When to Use

• Creating onboarding documentation for new developers
• Generating deployer guides for operations teams
• Understanding a new or unfamiliar codebase
• Documenting a product before release
• EU AI Act Articles 13 & 14 compliance (Transparency & Human Oversight)

Quick Start

python scripts/generate_training.py --path /path/to/repo

Instructions

1. Identify the target codebase: Determine the repository root to scan.

2. Run the generator:

   python "AI Act skills packages/AI Act package/deployer-training/scripts/generate_training.py" \
     --path <path-to-repository> \
     --name "Your Product Name"
   

3. Review the output: Check project root Output/Deployer_Guide.md for the generated documentation.

4. Human review: Always recommend human review for accuracy and completeness.

What You Get

A comprehensive Deployer_Guide.md with:

1. Executive Summary: High-level product overview.
2. System Architecture: Component diagrams (Mermaid), data flow, tech stack.
3. Product Capabilities: Core features, user journeys, configuration options.
4. Developer Onboarding: Environment setup, extension patterns, testing guidelines.
5. Operational Guide: Deployment strategy, troubleshooting & limitations.

Parameters

Parameter	Type	Default	Description
--path	string	.	Path to the repository root
--output	string	Deployer_Guide.md	Output file path
--model	string	Auto	Specific model (uses gemini-3-pro-preview then gemini-2.0-flash-exp as fallback)

Requirements

• Python 3.8+
• google-genai package: pip install google-genai
• GEMINI_API_KEY environment variable set

Best Practices

• Run on a clean checkout of the repository.
• The generator respects .gitignore and excludes .env files for security.
• For large codebases, review the "Total context size" output to ensure it fits in the model's context window.
• Always perform human review on generated documentation.

EU AI Act Compliance

This tool addresses:

• Article 13: Transparency and provision of information to users
• Article 14: Human oversight requirements (documentation for deployers)

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