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Orchestrate Production AI/ML Workflows

Seven-phase bundle orchestrating specialist skills across LLM integration, RAG, agents, ML pipelines, observability, and AI security.

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Works with langchaincrewailanggraphgemini apilangfuse
Antigravity
Maintainer?
91
Spark score
out of 100
Updated last month
Version 13.1.0
Models
gemini 1 5 pro
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Why it matters

Build and deploy sophisticated AI/ML applications, including LLM-powered features, RAG systems, and AI agents, with robust MLOps and observability.

Outcomes

What it gets done

01

Design and implement LLM applications and RAG systems.

02

Develop and orchestrate AI agents using frameworks like CrewAI and LangGraph.

03

Establish end-to-end ML pipelines with MLOps best practices.

04

Integrate AI observability for monitoring and evaluation.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-ai-ml | bash

Overview

AI/ML Workflow Bundle

A seven-phase bundle orchestrating specialist skills across LLM integration, RAG, AI agents, ML pipelines, observability, and AI security. Use as the roadmap for a comprehensive AI/ML build; each phase delegates implementation to a dedicated specialist skill rather than doing it directly.

What it does

This workflow bundle orchestrates a comprehensive AI/ML build across seven phases, each naming specific skills to invoke: Phase 1 AI Application Design (ai-product, ai-engineer, ai-agents-architect, llm-app-patterns - define use cases, model choice, architecture, success metrics); Phase 2 LLM Integration (ai-assistant dev, LangChain agents, prompt optimization, Gemini API - provider selection, prompt templates, streaming, error handling); Phase 3 RAG Implementation (rag-engineer, embedding-strategies, vector-database-engineer, hybrid-search-implementation - data pipeline, chunking, retrieval, reranking, caching); Phase 4 AI Agent Development (autonomous-agents, crewai, langgraph, multi-agent-patterns, computer-use-agents - roles, tool integration, memory, orchestration, human-in-the-loop); Phase 5 ML Pipeline Development (ml-engineer, mlops-engineer, data-engineer - training, evaluation, model registry, deployment); Phase 6 AI Observability (langfuse, manifest, llm-evaluation - tracing, logging, cost tracking, alerts); and Phase 7 AI Security (prompt-engineering security, SAST scanning - input validation, output filtering, rate limiting, access controls, audit logging).

It includes checklists for LLM integration (API keys secured, rate limiting, streaming, token tracking), RAG systems (pipeline working, embeddings generated, retrieval accuracy tested, caching implemented), AI agents (roles defined, tools integrated, memory working, orchestration tested), and observability (tracing, metrics, evaluation, alerts, dashboards), plus overall quality gates before considering the work done.

When to use - and when NOT to

Use this as the overarching roadmap when building LLM-powered applications, implementing RAG, creating AI agents, developing ML pipelines, adding AI features to an existing application, or setting up AI observability - anywhere multiple AI-related concerns need to be sequenced together.

Not a substitute for the specialist skills it names - each phase delegates the actual implementation work to a dedicated skill (rag-engineer, crewai, langgraph, ml-engineer, langfuse, etc.). Use this to know what to build in what order and which skill handles each piece, not as the implementation itself.

Inputs and outputs

Inputs: the target AI feature or system scope (LLM app, RAG, agents, ML pipeline, or a combination) and the constraints/success metrics for each.

Outputs: a full AI system spanning LLM integration, RAG (if applicable), agents (if applicable), ML pipelines (if applicable), observability, and security controls - each phase checked against its own quality checklist before moving to the next.

Integrations

Delegates to ai-product, ai-engineer, ai-agents-architect, rag-engineer, vector-database-engineer, crewai, langgraph, ml-engineer, mlops-engineer, langfuse, and prompt-engineering security skills, among others; related bundles include development, database, cloud-devops, and testing-qa.

Who it's for

Teams planning a comprehensive AI/ML build spanning multiple concerns - LLM integration, RAG, agents, ML pipelines, observability, and security - who need a sequenced roadmap naming the right specialist skill at each phase.

Source README

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

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
  2. Define agent roles
  3. Implement tool integration
  4. Set up memory systems
  5. Configure orchestration
  6. Add human-in-the-loop
Copy-Paste Prompts
Use @crewai to build role-based multi-agent system

Use @langgraph to create stateful AI workflows

Use @autonomous-agents to design autonomous agent

Phase 5: ML Pipeline Development

Skills to Invoke
  • ml-engineer - ML engineering
  • mlops-engineer - MLOps
  • machine-learning-ops-ml-pipeline - ML pipelines
  • ml-pipeline-workflow - ML workflows
  • data-engineer - Data engineering
Actions
  1. Design ML pipeline
  2. Set up data processing
  3. Implement model training
  4. Configure evaluation
  5. Set up model registry
  6. Deploy models
Copy-Paste Prompts
Use @ml-engineer to build machine learning pipeline

Use @mlops-engineer to set up MLOps infrastructure

Phase 6: AI Observability

Skills to Invoke
  • langfuse - Langfuse observability
  • manifest - Manifest telemetry
  • evaluation - AI evaluation
  • llm-evaluation - LLM evaluation
Actions
  1. Set up tracing
  2. Configure logging
  3. Implement evaluation
  4. Monitor performance
  5. Track costs
  6. Set up alerts
Copy-Paste Prompts
Use @langfuse to set up LLM observability

Use @evaluation to create evaluation framework

Phase 7: AI Security

Skills to Invoke
  • prompt-engineering - Prompt security
  • security-scanning-security-sast - Security scanning
Actions
  1. Implement input validation
  2. Add output filtering
  3. Configure rate limiting
  4. Set up access controls
  5. Monitor for abuse
  6. Implement audit logging

AI Development Checklist

LLM Integration

  • API keys secured
  • Rate limiting configured
  • Error handling implemented
  • Streaming enabled
  • Token usage tracked

RAG System

  • Data pipeline working
  • Embeddings generated
  • Vector search optimized
  • Retrieval accuracy tested
  • Caching implemented

AI Agents

  • Agent roles defined
  • Tools integrated
  • Memory working
  • Orchestration tested
  • Error handling robust

Observability

  • Tracing enabled
  • Metrics collected
  • Evaluation running
  • Alerts configured
  • Dashboards created

Quality Gates

  • All AI features tested
  • Performance benchmarks met
  • Security measures in place
  • Observability configured
  • Documentation complete

Related Workflow Bundles

  • development - Application development
  • database - Data management
  • cloud-devops - Infrastructure
  • testing-qa - AI testing

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

FAQ

Common questions

Discussion

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