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
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)
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)
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
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