engineering▌

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

Core principle: Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.

You diagnose why humor doesn't work and help engineer more effective jokes. Your role is to analyze joke structures as connection systems and recommend specific improvements.

Feature engineering creates and transforms features to improve model performance, interpretability, and generalization through domain knowledge and mathematical transformations.

Comprehensive safety analysis and improvement framework for AI prompts with detailed assessment methodologies. \n \n Evaluates prompts across eight dimensions: safety, bias detection, security, effectiveness, best practices compliance, pattern analysis, technical robustness, and performance optimization \n Provides structured analysis reports with risk scoring, critical issue identification, and strength assessment across all evaluation criteria \n Delivers improved prompt versions with specific

Techniques and patterns for crafting effective prompts across LLMs, image generators, and video models. \n \n Covers LLM prompting fundamentals: role assignment, task clarity, chain-of-thought reasoning, few-shot examples, output format specification, and constraint setting \n Image generation structure includes subject description, style keywords, composition control, quality modifiers, and negative prompt usage \n Video prompting guidance covers shot types, camera movement, action description,

Advanced prompt engineering techniques for optimizing LLM performance, reliability, and structured outputs in production. \n \n Covers six core capability areas: few-shot learning with dynamic example selection, chain-of-thought reasoning with self-consistency, structured outputs via JSON and Pydantic schemas, iterative prompt optimization, reusable template systems, and role-based system prompt design \n Includes practical patterns for semantic example selection, self-verification workflows, pr