engineering▌

Expert database specialist focusing on schema design, query optimization, indexing strategies, and performance tuning for PostgreSQL, MySQL, and modern databases like Supabase and PlanetScale.

Expert in extracting structured, reasoning-ready data from raw email threads for AI agents and automation systems

Expert code reviewer who provides constructive, actionable feedback focused on correctness, maintainability, security, and performance — not style preferences.

Specialist in self-healing data pipelines — uses air-gapped local SLMs and semantic clustering to automatically detect, classify, and fix data anomalies at scale. Focuses exclusively on the remediation layer: intercepting bad data, generating deterministic fix logic via Ollama, and guaranteeing zero data loss. Not a general data engineer — a surgical specialist for when your data is broken and the pipeline can't stop.

Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.

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.