ai-ml

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• SKILL.md

SKILL.md (first lines)

# AI/ML Workflow Bundle

## Overview

Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.

## When to Use This Workflow

Use this workflow when:
- Building LLM-powered applications
- Implementing RAG (Retrieval-Augmented Generation)
- Creating AI agents
- Developing ML pipelines
- Adding AI features to applications
- Setting up AI observability

## Selection and completion

Select only phases needed for the requested product; this workflow does not authorize delegation, hosted calls, deployment, background jobs or external messages. Read the actual selected skill and available tool schema. Define task-level quality, retrieval/source checks, data permissions, latency/currency limits and failure recovery before implementation. Verify each used capability in the target environment; unchecked checklist items are not completed because an instruction exists.

## Workflow Phases

### Phase 1: AI Application Design

#### Skills to Invoke
- `ai-product` - AI product development
- `ai-engineer` - AI engineering
- `ai-agents-architect` - Agent architecture
- `llm-app-patterns` - LLM patterns

#### Actions
1. Define AI use cases
2. Choose appropriate models
3. Design system architecture
4. Plan data flows
5. Define success metrics

#### Copy-Paste Prompts
```
Use @ai-product to design AI-powered features
```

```
Use @ai-agents-architect to design multi-agent system
```

### Phase 2: LLM Integration

#### Skills to Invoke
- `llm-application-dev-ai-assistant` - AI assistant development
- `llm-application-dev-langchain-agent` - LangChain agents
- `llm-application-dev-prompt-optimize` - Prompt engineering
- `gemini-api-dev` - Gemini API

#### Actions
1. Select LLM provider
2. Set up API access
3. Implement prompt templates
4. Configure model parameters
5. Add streaming support
6. Implement error handling

#### Copy-Paste Prompts
```
Use @llm-application-dev-ai-assistant to build conversational AI
```

```
Use @llm-application-dev-langchain-agent to create LangChain agents
```

```
Use @llm-application-dev-prompt-optimize to optimize prompts
```

### Phase 3: RAG Implementation

#### Skills to Invoke
- `rag-engineer` - RAG engineering
- `rag-implementation` - RAG implementation
- `embedding-strategies` - Embedding selection
- `vector-database-engineer` - Vector databases
- `similarity-search-patterns` - Similarity search
- `hybrid-search-implementation` - Hybrid search

#### Actions
1. Design data pipeline
2. Choose embedding model
3. Set up vector database
4. Implement chunking strategy
5. Configure retrieval
6. Add reranking
7. Implement caching

#### Copy-Paste Prompts
```
Use @rag-engineer to design RAG pipeline
```

```
Use @vector-database-engineer to set up vector search
```

```
Use @embedding-strategies to select optimal embeddings
```

### Phase 4: AI Agent Development

#### Skills to Invoke
- `autonomous-agents` - Autonomous agent patterns
- `autonomous-agent-patterns` - Agent patterns
- `crewai` - CrewAI framework
- `langgraph` - LangGraph
- `multi-agent-patterns` - Multi-agent systems
- `computer-use-agents` - Computer use agents

#### Actions
1. Design agent architecture

From sickn33/agentic-awesome-skills, MIT.

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