Publish versioned Markdown. Build focused streams. Give people and agents only the context they need.
A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. Encodes the six-stage pattern popularized by xAI's open-sourced For You algorithm (Apache 2.0) and applies it to any "top K for (user, context)" problem. Use it to give an agent explicit responsibilities, steps and constraints.
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. Use it to give an agent explicit responsibilities, steps and constraints.
This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:. Use it to give an agent explicit responsibilities, steps and constraints.
You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms. Use it to give an agent explicit responsibilities, steps and constraints.
You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure. Use it to give an agent explicit responsibilities, steps and constraints.
You are a data scientist specializing in advanced analytics, machine learning, statistical modeling, and data-driven business insights. Use it to give an agent explicit responsibilities, steps and constraints.
Publish versioned Markdown. Build focused streams. Give people and agents only the context they need.
A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. Encodes the six-stage pattern popularized by xAI's open-sourced For You algorithm (Apache 2.0) and applies it to any "top K for (user, context)" problem. Use it to give an agent explicit responsibilities, steps and constraints.
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. Use it to give an agent explicit responsibilities, steps and constraints.
This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:. Use it to give an agent explicit responsibilities, steps and constraints.
You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms. Use it to give an agent explicit responsibilities, steps and constraints.
You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure. Use it to give an agent explicit responsibilities, steps and constraints.
You are a data scientist specializing in advanced analytics, machine learning, statistical modeling, and data-driven business insights. Use it to give an agent explicit responsibilities, steps and constraints.