Ai Ml Engineering Pack

Professional toolkit for building production-ready AI/ML systems with Claude Code

Master prompt engineering, LLM integration, RAG systems, and AI safety with 12 specialized plugins that accelerate AI development by 10x.

What's Included

12 specialized plugins across 4 AI/ML categories:

1. Prompt Engineering (3 plugins)

• prompt-architect (agent) - Expert in CoT reasoning, few-shot learning, and advanced prompt patterns
• prompt-optimizer (agent) - Reduce LLM costs by 60-90% while maintaining quality
• prompt-template-gen (command: /ptg) - Generate production-ready prompt templates with type safety

2. LLM Integration (3 plugins)

• llm-integration-expert (agent) - Production API patterns, error handling, streaming, rate limiting
• model-selector (agent) - Choose optimal models based on cost, quality, latency requirements
• llm-api-scaffold (command: /las) - Generate complete LLM API with FastAPI, Docker, monitoring

3. RAG Systems (3 plugins)

• rag-architect (agent) - Design RAG systems, chunking strategies, retrieval optimization
• vector-db-expert (agent) - Select and configure vector databases (Pinecone, Qdrant, Weaviate, etc.)
• rag-pipeline-gen (command: /rpg) - Generate complete RAG pipeline with embeddings and retrieval

4. AI Safety (3 plugins)

• ai-safety-expert (agent) - Content filtering, PII detection, bias mitigation, compliance
• prompt-injection-defender (agent) - Defend against prompt injection and jailbreak attacks
• ai-monitoring-setup (command: /ams) - Set up LLM monitoring, cost tracking, and alerts

Quick Start

Installation

# Add the marketplace (if not already added)
claude plugin marketplace add jeremylongshore/claude-code-plugins

# Install AI/ML Engineering Pack
claude plugin install ai-ml-engineering-pack@claude-code-plugins-plus

# Verify installation
claude plugin list

Full installation guide: INSTALLATION.md

10-Minute Tutorial

Build your first AI feature in 10 minutes:

# Start Claude Code
claude

# Inside Claude, optimize a prompt
"Optimize this prompt for cost and quality:
'I would like you to create a detailed product description for...'"
# Claude uses prompt-optimizer agent to reduce tokens by 70%

# Generate a reusable prompt template
/ptg

# Build a production LLM API
/las

# Create a complete RAG system
/rpg

# Add AI safety guardrails
…

Complete tutorial: QUICK_START.md

ROI & Value Proposition

Real-world results from production deployments:

Average ROI: 29,351% | Average payback period: 3 days

Detailed case studies: USE_CASES.md

Plugin Reference

Prompt Engineering

prompt-architect (Agent)

Expert in advanced prompt engineering techniques and patterns.

Capabilities:

• Chain-of-Thought (CoT) reasoning
• Few-shot and zero-shot learning
• Prompt composition patterns
• Meta-prompting and self-improvement
• Multi-modal prompts (text + images)

When to use:

• "Design a prompt for [complex task]"
• "Improve this prompt: [existing prompt]"
• "What's the best prompting technique for [use case]?"

Activation triggers: Prompt design, CoT, few-shot learning, prompt patterns

prompt-optimizer (Agent)

Optimize prompts for cost reduction (60-90% savings) while maintaining quality.

Capabilities:

• Token reduction techniques (remove verbosity, use abbreviations)
• Prompt caching strategies
• Model selection guidance (cheap vs expensive)
• Cost-quality trade-off analysis
• ROI calculation

When to use:

• "Reduce the cost of this prompt: [prompt]"
• "Optimize my prompts for $1000/month budget"
• "How can I reduce token usage by 70%?"

Example:

Before (52 tokens): "I would like you to please analyze..."
After (15 tokens): "Analyze and summarize main points."
Savings: 71% token reduction = $0.15/1000 calls (GPT-4)

Activation triggers: Cost optimization, token reduction, prompt efficiency

/ptg - Prompt Template Generator (Command)

Generate production-ready prompt templates with type safety and validation.

Usage:

/ptg

# Claude asks:
# - Use case (e.g., product descriptions, customer support, code review)
# - Variables (e.g., product_name, features, tone)
# - Output format (Python, TypeScript)
# - Validation requirements