engineering▌

Analyze and engineer protein glycosylation for therapeutic antibody optimization and vaccine design.

COBRApy is a Python library for constraint-based metabolic modeling, essential for systems biology research and metabolic engineering analysis.

Discovers and invokes agent skills for effective task management in engineering workflows.

Manages deprecation and migration. Use when removing old systems, APIs, or features.

Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.

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

Bridge design and engineering teams by clarifying roles, organizational placement, and hiring strategy. \n \n Defines design engineering as merging UX design with engineering to accelerate prototyping and production shipping, with scope varying from validation prototypes to production-ready code \n Emphasizes combining design sensibility with shipping ability rather than seeking unicorn-level expertise in both disciplines \n Guides organizational placement decisions, handoff friction diagnosis,

Diagnose whether your AI workflows are context stuffing or context engineering, then implement structured practices to improve output quality. \n \n Distinguishes between context stuffing (volume-based) and context engineering (structure-based), with five diagnostic questions to identify Context Hoarding Disorder \n Guides two-layer memory architecture: short-term conversational memory plus long-term persistent memory via vector databases for semantic retrieval \n Implements the Research→Plan→Re

Comprehensive PostgreSQL database engineering covering indexing, query optimization, replication, and production management. \n \n Covers 6 index types (B-tree, Hash, GiST, GIN, BRIN, SP-GiST) with decision matrices for choosing the right index for specific query patterns \n Includes streaming and logical replication setup, failover procedures, and high-availability configuration with synchronous/asynchronous modes \n Provides partitioning strategies (range, list, hash) with automation examples

Engineering operating model for teams shipping code with AI-assisted generation. \n \n Emphasizes planning quality, evaluation coverage, and behavior-focused code review over traditional syntax checking and typing speed \n Requires agent-friendly architectures with explicit boundaries, stable contracts, typed interfaces, and deterministic tests to minimize implicit behavior \n Shifts review priorities to behavior regressions, security assumptions, data integrity, failure handling, and rollout sa