Skill UI

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

This skill covers the full lifecycle of deploying machine learning models into production environments. It focuses on establishing robust MLOps pipelines, including model deployment workflows (e.g., Docker, Canary releases), feature store management (Feast), and comprehensive drift monitoring. Furthermore, it details advanced integration patterns for Large Language Models (LLMs) and building Retrieval-Augmented Generation (RAG) systems, emphasizing operational stability, performance validation, and cost optimization.

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

A comprehensive skill to identify, analyze, prioritize, and track technical debt across large codebases. It provides three interconnected tools: a Debt Scanner, a Debt Prioritizer (using cost-of-delay frameworks), and a Debt Dashboard. This system helps engineering teams make data-driven decisions to balance new features with necessary maintenance and refactoring work, ensuring the long-term sustainability of software systems.

Guides multi-location retailers through demand forecasting, safety stock calculation, replenishment planning, promotional lift estimation, and ABC/XYZ segmentation, blending statistical best practices with ML-ready feature engineering to balance service levels and inventory costs.

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

An expert guide for reducing large-scale LLM API costs (40-80%). Learn engineering techniques like model routing, prompt caching, and output length control to make every token count. Essential for engineers building cost-efficient AI features and robust endpoints.

This skill applies principles of behavioral psychology to redesign user onboarding flows. Instead of simple feature tours, it focuses on engineering the first-use experience to achieve 'early wins,' build competence, establish ownership, and trigger habit formation. Use it when the goal is to reduce user friction, combat early drop-off, and shift the user's self-perception from an outsider to an active participant.

Expert skill for generating production-quality Python scripts specifically for the FreeCAD CAD application. This includes automating complex 3D modeling tasks, creating parametric FeaturePython objects, performing advanced geometric operations (Boolean, Fillet, Loft), building custom GUI tools using PySide, and handling mesh/FEM analyses via the FreeCAD Python API. Ideal for CAD automation and engineering visualization.

A comprehensive guide for engineers, architects, and DevOps teams on implementing General Data Protection Regulation (GDPR) best practices. This skill covers Privacy by Design, data minimization, purpose limitation, retention policies, secure API design, and compliant logging practices. Use it when handling any personal data, designing new features, or reviewing systems for data privacy compliance.

Manage feature flags end-to-end with lifecycle discipline, debt scanning, and rollout planning. Includes Python tools for auditing stale flags, planning progressive rollouts, and verifying kill-switch documentation. Covers taxonomy, provider selection, and release engineering best practices.