Chaos Engineering

Use when implementing chaos engineering, designing fault injection experiments, or building resilience testing practices. Covers chaos principles and experiment design.

Craft effective prompts that get the best results from language models.

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You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

Use when designing agent system prompts, optimizing RAG retrieval, or when context is too expensive or slow. Reduces tokens while maintaining quality through strategic positioning and attention-aware design.

Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter

Practical guide for building production ML systems based on Chip Huyen's AI Engineering book. Use when users ask about model evaluation, deployment strategies, monitoring, data pipelines, feature engineering, cost optimization, or MLOps. Covers metrics, A/B testing, serving patterns, drift detection, and production best practices.

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication.

AI-powered marketing engineering skill based on Alon Huri's framework. Transforms marketing from copywriting to engineering discipline through 10 agentic mechanisms: infinite creative generation, adaptive budget management, LTV signal hunting, contextual data layers, AEO optimization, dynamic quizzes, behavior-driven activation, personalized video at scale, competitor weakness targeting, and active churn prevention. Use when building marketing automation systems, designing growth engineering workflows, creating AI-powered marketing agents, optimizing ad creatives at scale, implementing AEO (Answer Engine Optimization), or architecting data-driven marketing infrastructure.

Operate the "Engineering Multi-Agent Negotiation Mediator" capability in production for workflows. Use when mission execution explicitly requires this capability and outcomes must be reproducible, policy-gated, and handoff-ready.

Write effective prompts for AI coding agents. Use when crafting system prompts, implementing chain-of-thought reasoning, building few-shot examples, adding guardrails, configuring tool use, or designing agentic prompt patterns. Covers CoT, few-shot, guardrails, and function calling.