engineering▌

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

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

Advanced techniques for maximizing LLM reliability, consistency, and output quality through systematic prompt design. \n \n Five core patterns: few-shot learning with 2-5 examples, chain-of-thought reasoning for multi-step logic, systematic optimization through A/B testing, reusable template systems with variables, and system prompts for persistent global behavior \n Progressive disclosure approach starts simple and adds complexity only when needed, with four escalation levels from direct instru

Advanced prompt patterns for few-shot learning, chain-of-thought reasoning, optimization, templates, and system prompt design. \n \n Covers five core pattern categories: few-shot example selection with semantic diversity, chain-of-thought reasoning traces, iterative optimization workflows with A/B testing, modular template systems with variable interpolation, and comprehensive system prompt architecture \n Includes structured implementation workflows for creating new prompts, optimizing existing

25 advanced engineering skills for complex architecture, automation, and platform operations.

Expert guide for optimizing LLM prompts through patterns, testing, and systematic refinement. \n \n Covers five core techniques: few-shot learning, chain-of-thought reasoning, prompt optimization, template systems, and system prompt design \n Includes progressive disclosure patterns that scale complexity from simple instructions to multi-example reasoning traces \n Provides practical examples for each pattern, from code review templates to bug analysis workflows \n Emphasizes testing and iterati

Build strong engineering culture using frameworks from 19 product leaders. \n \n Diagnose current state across team size, practices, and pain points, then identify bottlenecks in developer experience, org structure, talent, or process \n Core principle: Conway's Law means organizational structure directly dictates architecture and product quality; align teams to desired outcomes \n DevEx is foundational; optimize for flow state, cognitive load, and feedback loops rather than toolchain selection

Structured guidance for building production AI agent systems through effective context management and multi-agent architectures. \n \n Covers foundational context engineering concepts including attention degradation patterns, context poisoning, and signal-to-noise optimization for language models \n Provides architectural patterns for multi-agent coordination (supervisor, peer-to-peer, hierarchical), memory system design, and filesystem-based context management \n Includes operational excellence

AI-driven engineering workflows with eval-first execution, task decomposition, and cost-aware model routing. \n \n Defines an eval-first loop: establish baseline evals before implementation, then re-run post-execution to measure deltas and catch regressions \n Decomposes work into 15-minute units with single dominant risks, independent verifiability, and clear done conditions \n Routes tasks by complexity: Haiku for classification and boilerplate, Sonnet for implementation, Opus for architecture

Capture, analyze, and document network protocols through packet inspection and binary dissection. \n \n Covers traffic capture with Wireshark, tcpdump, and mitmproxy, including transparent interception and ring-buffer rotation for continuous monitoring \n Provides protocol analysis techniques: display filtering, stream following, field extraction, and TLS decryption with pre-master-secret logs \n Includes binary protocol parsing patterns (length-prefixed, TLV, fixed-header) with Python struct un