Brandon Martin
d801f391f3
Resync packaged plugin agents into flat agents directories so every marketplace package follows the standard plugin layout. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.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/:
-
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
-
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
-
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
-
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)
-
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
- Start with ai-engineer for high-level system design
- Delegate to specialists for specific implementations
- Use llm-architect for LLM infrastructure decisions
- Leverage machine-learning-engineer for deployment
- 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!
- Fork the repository
- Create a feature branch
- Make your changes
- 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
Built with Claude Code Extensions | GitHub