Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.

ML Engineer Orchestrator

Communication: User's language. Code: English.

Purpose

Routes AI/ML tasks to specialized sub-skills. Orchestrates LLM integration, RAG, operations, agents, and multimodal AI.

Context Discovery

Auto-Investigation

Check for existing AI/ML setup:

Signal	How to Check	What It Tells Us
`openai` in dependencies	`Grep("openai", "**/requirements.txt")`	OpenAI SDK used
`anthropic` in dependencies	`Grep("anthropic", "**/requirements.txt")`	Claude SDK used
`langchain` in dependencies	`Grep("langchain", "**/requirements.txt")`	LangChain framework
`llamaindex` in dependencies	`Grep("llama-index", "**/requirements.txt")`	LlamaIndex framework
Vector DB config	`Grep("qdrant\|chroma\|pinecone\|weaviate", "*/")`	Vector DB setup exists
Embedding models	`Grep("embed\|embedding", "*/.py")`	Embeddings used
`.env` with API keys	`Grep("OPENAI_API_KEY\|ANTHROPIC_API_KEY", "*/.env")`	Which APIs configured

Discovery Questions

Use AskUserQuestion to understand AI/ML requirements.

Q1: AI/ML Goal

yaml

question: "What do you want to achieve with AI/ML?"
header: "Goal"
multiSelect: false
options:
  - label: "Use LLM APIs (chat, generation)"
    description: "Integrate OpenAI, Claude, or Gemini"
  - label: "Build RAG system (knowledge base)"
    description: "Search and retrieve from documents"
  - label: "Create AI agent (autonomous tasks)"
    description: "Agent that uses tools and reasons"
  - label: "Fine-tune a model"
    description: "Train model on custom data"
  - label: "Add vision/image/voice"
    description: "Multimodal AI capabilities"

Routing:

•"LLM APIs" → Skill(faion-llm-integration)
•"RAG system" → Skill(faion-rag-engineer)
•"AI agent" → Skill(faion-ai-agents)
•"Fine-tune" → Skill(faion-ml-ops)
•"Multimodal" → Skill(faion-multimodal-ai)

Q2: LLM Provider Preference (if LLM task)

yaml

question: "Which LLM provider do you prefer?"
header: "Provider"
multiSelect: false
options:
  - label: "OpenAI (GPT-4)"
    description: "Best general purpose, good tools support"
  - label: "Anthropic (Claude)"
    description: "Best for long context, reasoning, safety"
  - label: "Google (Gemini)"
    description: "Multimodal, 2M context, grounding"
  - label: "Local (Ollama)"
    description: "Privacy, no API costs, offline"
  - label: "Not sure / recommend"
    description: "I'll suggest based on your use case"

Q3: Data Situation (if RAG or fine-tuning)

yaml

question: "What data do you have?"
header: "Data"
multiSelect: true
options:
  - label: "Documents (PDF, markdown, text)"
    description: "Unstructured text content"
  - label: "Structured data (database, CSV)"
    description: "Tabular or relational data"
  - label: "Code repositories"
    description: "Source code to search/understand"
  - label: "Conversation logs"
    description: "Chat history, support tickets"

Routing:

•"Documents" → RAG with chunking strategies
•"Structured data" → Text-to-SQL or structured RAG
•"Code repos" → Code embeddings, AST-aware chunking
•"Conversations" → Fine-tuning dataset prep

Q4: Deployment Requirements

yaml

question: "How will this be deployed?"
header: "Deploy"
multiSelect: false
options:
  - label: "API endpoint (backend service)"
    description: "Part of web application"
  - label: "CLI tool"
    description: "Command-line interface"
  - label: "Batch processing"
    description: "Process data in bulk"
  - label: "Real-time/streaming"
    description: "Live interactions, low latency"

Context impact:

•"API endpoint" → Async patterns, rate limiting, caching
•"CLI tool" → Simple integration, local models option
•"Batch processing" → Cost optimization, parallel processing
•"Real-time" → Streaming responses, edge deployment

Sub-Skills (5)

Sub-Skill	Purpose	Methodologies
faion-llm-integration	LLM APIs, prompting, function calling	26
faion-rag-engineer	RAG systems, embeddings, vector search	22
faion-ml-ops	Fine-tuning, evaluation, cost, observability	15
faion-ai-agents	Autonomous agents, multi-agent, MCP	26
faion-multimodal-ai	Vision, image/video gen, speech, TTS	12

Total: 101 methodologies

Routing Logic

Task Type	Route To
OpenAI/Claude/Gemini API integration	faion-llm-integration
Prompt engineering, CoT, guardrails	faion-llm-integration
RAG pipeline, embeddings, chunking	faion-rag-engineer
Vector databases, hybrid search	faion-rag-engineer
Fine-tuning, LoRA, evaluation	faion-ml-ops
Cost optimization, observability	faion-ml-ops
Agents, multi-agent, LangChain	faion-ai-agents
MCP, agent architectures	faion-ai-agents
Vision, image/video generation	faion-multimodal-ai
Speech-to-text, TTS, voice	faion-multimodal-ai

Execution Protocol

When a task arrives:

•Analyze task intent
•Select appropriate sub-skill (use routing table above)
•Invoke sub-skill with Skill tool
•Return results to caller

Quick Reference

Provider	Best For	Context	Sub-Skill
OpenAI	General, vision, tools	128K	faion-llm-integration
Claude	Long context, reasoning	200K	faion-llm-integration
Gemini	Multimodal, 2M context	2M	faion-llm-integration
Local	Privacy, offline	Varies	faion-llm-integration

Task	Sub-Skill
RAG pipeline	faion-rag-engineer
Vector DB (Qdrant, Weaviate)	faion-rag-engineer
Fine-tuning	faion-ml-ops
Cost optimization	faion-ml-ops
Agents (ReAct, multi-agent)	faion-ai-agents
LangChain/LlamaIndex	faion-ai-agents
Vision, image gen	faion-multimodal-ai
Speech, TTS	faion-multimodal-ai

Related Skills

Skill	Relationship
faion-software-developer	Application integration
faion-devops-engineer	Model deployment

ML Engineer Orchestrator v2.0 5 Sub-Skills | 101 Total Methodologies