engineering▌

This skill covers reverse engineering workflows for game security research, including protected game clients, anti-cheat user-mode modules, kernel drivers, memory artifacts, and debugging environments that must survive anti-analysis checks.

AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. Covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

Master prompt engineering for AI models via inference.sh CLI.

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