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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

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Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML

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Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

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Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

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Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

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Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository. Use when creating skill tests, writing evaluation scenarios, defining assertions and rubrics, or setting up test fixture files. Handles eval.yaml generation, fixture organization, and overfitting avoidance. Do not use for running or debugging existing tests nor for skills authoring.

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Choose typefaces based on voice and context, not trends. Use when selecting fonts for a project, evaluating type choices, or understanding what a typeface communicates before anyone reads a word.

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The 10-second design audit. Look at any design and name what's working and what's not within seconds. Trains rapid pattern recognition for hierarchy, spacing, type, and color. Use when evaluating designs quickly, giving first-impression feedback, or building perception speed.

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Tell the difference between design decisions that age well and decisions that follow the crowd. Use when evaluating whether a design choice is principled or fashionable, or when a client asks to 'make it look modern.'

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Animation as communication. Feedback, orientation, emphasis, delight. If motion doesn't serve one of these four purposes, it shouldn't exist. Use when evaluating animation quality, designing transitions, or deciding whether motion adds or subtracts.

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Use taste as a competitive moat and business advantage. In the AI and vibe-coding era, execution is commoditized. Taste is the defensible edge. Use when advising founders on product differentiation, building product culture, evaluating why some products win despite fewer features, or understanding taste as a strategic asset.

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Micro-decision analysis. Catalog the border radii, spacing scales, type sizes, and state coverage that most people miss. Use when auditing design systems, evaluating craft quality, or training granular perception.

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Visual weight, attention order, and the decisions that guide the eye. Use when building layouts, evaluating information architecture, or diagnosing why a design feels confusing.

日本語の概要は準備中です。原文の説明を表示しています。

Density, whitespace, rhythm, and how space communicates relationships between elements. Use when building layouts, evaluating spacing consistency, or diagnosing why a design feels cramped or empty.

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Rank multiple design solutions and explain why. Separate personal preference from quality judgment. Use when comparing design options, evaluating competing approaches, or making design decisions with clear reasoning.

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Palette roles, restraint, semantic consistency, and when fewer colors means better design. Use when building color palettes, evaluating color usage, or diagnosing color-related design problems.

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Task sequences, progressive disclosure, and the pacing of an experience over time. Use when designing multi-step flows, evaluating onboarding, or diagnosing why users get lost.

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Read a designer's taste through their typography decisions alone. Type is the fastest taste signal in any design. Use when evaluating design maturity, assessing portfolio quality, or understanding what typography reveals about design thinking.

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Consistent radii, pixel-perfect alignment, state coverage, and the details that signal design quality. Use when polishing designs, auditing quality, or evaluating whether a design is ready to ship.

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Notice what's deliberately missing from a design. Restraint is a taste signal. Use when studying minimalist products, evaluating design maturity, or understanding the power of removal.

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Name what was prioritized, what was sacrificed, and whether the tradeoff was right. Every design decision trades something. Use when evaluating design decisions, defending choices, or helping teams understand what they're giving up.

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Response timing, acknowledgment, and making the interface feel alive. Every action deserves a response at the right speed. Use when designing interactions, evaluating responsiveness, or diagnosing why a product feels sluggish or dead.

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Design and evaluate programmatic SEO strategies for creating SEO-driven pages at scale using templates and structured data.

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LLM Agent engineering: pattern selection (12-mode matrix), data simulation, convergence iteration. Covers intent routing, function calling, ReAct, MCP, prompt chaining, guardrails, evaluation.

日本語の概要は準備中です。原文の説明を表示しています。