nodnarbnitram__claude-code-extensions/plugins/cce-ai

cce-ai - AI/ML Development Plugin

Version: 1.0.0 Status: Production Ready Category: AI/ML Development

Overview

The cce-ai plugin provides comprehensive AI/ML development capabilities through specialized agents covering the full spectrum of modern AI development: from LLM architecture and prompt engineering to NLP systems and production ML deployment.

This plugin is designed for teams building AI-powered applications, training and deploying machine learning models, implementing LLM-based systems, or developing natural language processing solutions.

What's Included

Agents (5 specialists)

All agents are located in ${CLAUDE_PLUGIN_ROOT}/agents/specialized/data-ai/:

1. ai-engineer - Senior AI engineer for comprehensive AI system design

   • AI architecture design and model selection
   • Training pipeline development
   • Production deployment and monitoring
   • Ethical AI and governance frameworks
   • Multi-modal systems integration

2. llm-architect - LLM system design and optimization expert

   • Large language model architecture
   • Fine-tuning strategies (LoRA/QLoRA, RLHF)
   • RAG implementation and optimization
   • Serving infrastructure (vLLM, TGI, Triton)
   • Safety mechanisms and cost optimization

3. machine-learning-engineer - Production ML deployment specialist

   • Model optimization (quantization, pruning, distillation)
   • Real-time inference infrastructure
   • Batch prediction systems
   • Auto-scaling and monitoring
   • Edge deployment strategies

4. nlp-engineer - Natural language processing expert

   • Text preprocessing and tokenization
   • Named entity recognition and classification
   • Machine translation and question answering
   • Sentiment analysis and information extraction
   • Multilingual support (12+ languages)

5. prompt-engineer - Prompt design and optimization specialist

   • Prompt architecture and patterns
   • Few-shot learning and chain-of-thought
   • A/B testing and evaluation frameworks
   • Token optimization and cost reduction
   • Production prompt management

Use Cases

LLM Applications

• Design and deploy scalable LLM systems
• Implement RAG (Retrieval-Augmented Generation)
• Fine-tune models for specific domains
• Optimize inference performance and costs
• Build conversational AI and chatbots

Machine Learning Operations

• Deploy models to production with monitoring
• Optimize model size and inference latency
• Implement auto-scaling ML services
• Build batch prediction pipelines
• Deploy models to edge devices

Natural Language Processing

• Build multilingual NLP systems
• Implement named entity recognition
• Create sentiment analysis pipelines
• Develop question answering systems
• Extract information from text at scale

Prompt Engineering

• Design effective prompt templates
• Optimize token usage and costs
• Implement chain-of-thought reasoning
• A/B test prompt variations
• Manage production prompt systems

AI Engineering

• Design end-to-end AI systems
• Select and train models
• Implement ethical AI frameworks
• Build multi-modal AI applications
• Ensure AI governance and compliance

Agent Collaboration

The agents in this plugin are designed to work together:

ai-engineer
    ├── Collaborates with llm-architect on LLM integration
    ├── Supports machine-learning-engineer on deployment
    ├── Works with nlp-engineer on language tasks
    └── Guides prompt-engineer on LLM systems

llm-architect
    ├── Supports prompt-engineer on optimization
    ├── Works with machine-learning-engineer on serving
    └── Guides backend-developer on API design

machine-learning-engineer
    ├── Collaborates with ai-engineer on model selection
    ├── Supports mlops-engineer on infrastructure
    └── Works with data-engineer on pipelines

nlp-engineer
    ├── Collaborates with ai-engineer on model architecture
    ├── Works with machine-learning-engineer on deployment
    └── Assists prompt-engineer on language models

prompt-engineer
    ├── Collaborates with llm-architect on system design
    ├── Supports ai-engineer on LLM integration
    └── Works with data-scientist on evaluation

Installation

From Marketplace (Recommended)

# Add the marketplace (if not already added)
/plugin marketplace add https://github.com/nodnarbnitram/claude-code-extensions

# Install the plugin
/plugin install cce-ai@cce-marketplace

From Local Path (Development)

# Clone the repository
git clone https://github.com/nodnarbnitram/claude-code-extensions.git

# Add local marketplace
/plugin marketplace add /path/to/claude-code-extensions

# Install from local path
/plugin install cce-ai@cce-marketplace

Usage

Once installed, agents will automatically activate based on your tasks:

Automatic Agent Selection

The agents will automatically engage when you mention AI/ML related tasks:

> Help me implement a RAG system for document search
# llm-architect will engage

> Optimize this model for production deployment
# machine-learning-engineer will engage

> Design prompts for better LLM performance
# prompt-engineer will engage

> Build a multilingual sentiment analysis pipeline
# nlp-engineer will engage

> Design an end-to-end AI system for recommendation
# ai-engineer will engage

Manual Agent Invocation

You can also explicitly request specific agents:

> @ai-engineer design a training pipeline for image classification

> @llm-architect help me fine-tune this model with LoRA

> @machine-learning-engineer optimize inference latency

> @nlp-engineer implement named entity recognition

> @prompt-engineer create few-shot examples for this task

Agent Capabilities

ai-engineer

Color: Yellow Focus: Comprehensive AI system design and implementation

Key Capabilities:

• Model architecture selection and design
• Training pipeline development
• Inference optimization techniques
• Multi-modal systems integration
• Ethical AI and bias detection
• AI governance frameworks
• Edge AI deployment

Tools: TensorFlow, PyTorch, JAX, ONNX, TensorRT, Core ML

llm-architect

Color: Blue Focus: Large language model systems and optimization

Key Capabilities:

• Fine-tuning strategies (LoRA/QLoRA, RLHF)
• RAG implementation and optimization
• Serving patterns (vLLM, TGI, Triton)
• Model quantization and optimization
• Safety mechanisms and content filtering
• Token optimization and cost control
• Multi-model orchestration

Tools: Transformers, LangChain, LlamaIndex, vLLM, Weights & Biases

machine-learning-engineer

Color: Purple Focus: Production ML deployment and operations

Key Capabilities:

• Model optimization (quantization, pruning, distillation)
• Real-time inference infrastructure
• Batch prediction systems
• Auto-scaling strategies
• Multi-model serving
• Edge deployment
• Performance monitoring

Tools: TensorFlow, PyTorch, ONNX, Triton, BentoML, Ray, vLLM

nlp-engineer

Color: Orange Focus: Natural language processing systems

Key Capabilities:

• Text preprocessing pipelines
• Named entity recognition
• Text classification and sentiment analysis
• Machine translation (12+ languages)
• Question answering systems
• Information extraction
• Conversational AI

Tools: Transformers, spaCy, NLTK, Hugging Face, Gensim, FastText

prompt-engineer

Color: Pink Focus: Prompt design and optimization

Key Capabilities:

• Prompt architecture patterns
• Few-shot learning and chain-of-thought
• A/B testing frameworks
• Token optimization techniques
• Safety mechanisms
• Multi-model strategies
• Production prompt management

Tools: OpenAI API, Anthropic API, LangChain, PromptFlow, Jupyter

Performance Targets

Each agent follows strict performance benchmarks:

Agent	Metric	Target
ai-engineer	Model accuracy	> 94%
ai-engineer	Inference latency	< 100ms
llm-architect	Inference latency	< 200ms
llm-architect	Throughput	> 100 tokens/s
machine-learning-engineer	Inference latency	< 100ms
machine-learning-engineer	Throughput	> 1000 RPS
nlp-engineer	F1 score	> 0.85
nlp-engineer	Latency	< 100ms
prompt-engineer	Accuracy	> 90%
prompt-engineer	Response time	< 2s

Best Practices

When to Use Each Agent

ai-engineer: Use for overall AI system design, model selection, and comprehensive AI solutions requiring multiple components.

llm-architect: Use for LLM-specific tasks like fine-tuning, RAG implementation, or optimizing LLM serving infrastructure.

machine-learning-engineer: Use for deploying models to production, optimizing inference, or building scalable ML services.

nlp-engineer: Use for text processing tasks, language understanding, or multilingual NLP applications.

prompt-engineer: Use for optimizing LLM prompts, reducing token costs, or implementing prompt testing frameworks.

Workflow Recommendations

1. Start with ai-engineer for high-level system design
2. Delegate to specialists for specific implementations
3. Use llm-architect for LLM infrastructure decisions
4. Leverage machine-learning-engineer for deployment
5. Consult prompt-engineer for LLM interaction optimization

Examples

Example 1: Building a RAG System

> I need to build a RAG system for internal documentation search

# llm-architect engages and provides:
1. Document processing strategy
2. Embedding model selection
3. Vector store recommendation (e.g., Pinecone, Weaviate)
4. Retrieval optimization techniques
5. Context management strategies
6. Serving infrastructure design

Example 2: Optimizing Model Deployment

> This model is too slow in production. Latency is 500ms.

# machine-learning-engineer engages and:
1. Profiles model performance
2. Applies quantization (4-bit or 8-bit)
3. Implements model caching
4. Sets up batch processing
5. Configures auto-scaling
6. Reduces latency to < 100ms

Example 3: Prompt Optimization

> My prompts are using too many tokens and costing too much

# prompt-engineer engages and:
1. Analyzes current prompts
2. Implements token compression
3. Optimizes context usage
4. Creates A/B testing framework
5. Measures cost reduction (typically 30-50%)

Example 4: Multilingual NLP Pipeline

> Build a sentiment analysis system supporting 10 languages

# nlp-engineer engages and:
1. Designs preprocessing pipeline
2. Selects multilingual models
3. Implements language detection
4. Creates sentiment classification
5. Builds real-time API
6. Achieves > 0.85 F1 score across languages

Requirements

System Requirements

• Python 3.11+ (for AI/ML tools)
• GPU recommended for model training/inference
• 16GB+ RAM for LLM work
• Sufficient disk space for models

Optional Dependencies

• TensorFlow or PyTorch
• Hugging Face Transformers
• LangChain / LlamaIndex
• CUDA toolkit (for GPU acceleration)
• vLLM or other serving frameworks

Compatibility

• Claude Code Version: 0.1.0+
• Python: 3.11+
• Operating Systems: Linux, macOS, Windows (WSL2)
• GPU Support: NVIDIA CUDA 11.8+

Troubleshooting

Agents Not Appearing

# Verify plugin installation
/plugin list

# Check if agents are loaded
/agents

# Reinstall if needed
/plugin update cce-ai

Agent Not Auto-Engaging

Try explicit invocation with @agent-name syntax, or ensure your query includes relevant AI/ML keywords.

Performance Issues

Ensure you have:

• Adequate RAM (16GB+ for LLM work)
• GPU drivers installed (for GPU acceleration)
• Python 3.11+ with required dependencies

Contributing

This plugin is part of the Claude Code Extensions project. Contributions welcome!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

See CONTRIBUTING.md for details.

License

MIT License - see LICENSE for details.

Support

• Issues: https://github.com/nodnarbnitram/claude-code-extensions/issues
• Discussions: https://github.com/nodnarbnitram/claude-code-extensions/discussions
• Documentation: https://github.com/nodnarbnitram/claude-code-extensions

Changelog

v1.0.0 (Initial Release)

• 5 specialized AI/ML agents
• Complete LLM architecture support
• Production ML deployment capabilities
• Multilingual NLP processing
• Prompt engineering and optimization
• Full documentation and examples

Related Plugins

• cce-core: Essential extensions and hooks
• cce-kubernetes: Kubernetes operations and health checks
• cce-cloudflare: Cloudflare Workers and AI integration
• cce-esphome: IoT device configuration and management
• cce-web-react: React and frontend development

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