engineering▌

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.

A diagnostic skill that identifies code, architecture, and technical decisions built to impress rather than to ship. Vanity engineering is entropy disguised as craftsmanship — it increases complexity without proportional capability gain, and it compounds maintenance cost while delivering zero additional user value.

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.