Results for “feature selection”

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.

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Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".

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TanStack Table best practices for building headless, type-safe data tables in React with sorting, filtering, pagination, row selection, and column management. Use when building data grids, implementing client-side or server-side table features, defining column structures, managing table state, or optimizing table rendering performance.

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Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.

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Choose what kind of knowledge to transfer between teacher and student models: response, feature, or relational, and decide among offline, online, self, or cross-modal distillation schemes. Best for distillation strategy selection, capacity-gap diagnosis, and transfer planning. Activate on "knowledge distillation", "teacher-student", "soft labels", "feature distillation", "online distillation", or "cross-modal transfer". NOT for generic compression checklists or unrelated training work.

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Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the p>>n regime, batch shortcut learning, class imbalance, and probability calibration. Use when building a classifier from expression, methylation, or variant data, choosing an algorithm for high-dimensional small-n data, or diagnosing a suspiciously perfect AUC. For unbiased evaluation see machine-learning/model-validation; for feature selection see machine-learning/biomarker-discovery; for time-to-event outcomes see machine-learning/survival-analysis.

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TanStack Table (React Table) v9 ヘッドレステーブル (@tanstack/react-table) の API リファレンス。 ark-ui / chakra-ui のスタイル付き Table とは別。 useTable, createTableHook, tableFeatures, createColumnHelper, flexRender, createCoreRowModel, table.Subscribe, atoms, React Compiler, custom features, sorting, column / global / fuzzy filtering, faceting, pagination, row selection, expanding, grouping / aggregation, column / row の ordering・pinning・sizing・visibility, virtualization。 v8 legacy (useReactTable / getCoreRowModel) と v9 移行。

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Cross-app save-to-notes components and patterns. Use when adding save-to-notes to a feature, wiring overlay note capture, editing features/notes/actions/ or its consumers, or using QuickCaptureButton, SaveToScratchButton, SaveSelectionButton, QuickNotesSheet, CategoryNotesModal, or useOpenQuickNoteSaveOverlay.

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Build web search, page content retrieval, structured data extraction, and browser interactions into applications with Firecrawl. The skill guides endpoint selection, credentials, SDK or REST integration, and verification with a real request.

• Adding web search to an application
• Extracting structured data from known URLs
• Building website clicks and form filling into a workflow

Build MCP servers that let AI access actions and data using mcp-use. The guide covers project setup, reusable prompts, interactive React interfaces, authentication, error handling, and deployment, with reference files for each area.

• Create a new MCP server with mcp-use
• Add actions, data resources, or reusable prompts
• Build interactive React interfaces for browsing and selection

Build expressive web pages with wide headings, tightly filled grids, and GSAP animations that follow scrolling. Organize content from attention through action, vary layouts through a defined selection process, and provide static states for reduced-motion preferences.

• Building a landing page that leads visitors toward an action
• Creating galleries with scroll-linked motion
• Designing feature grids without empty cells

Build an evaluation process for AI agents using success, reliability, cost, and latency measures. Define scoring criteria, repeatable environments, failure analysis, and regression tests that check for recurring problems to support model selection and system improvements.

• Building an initial agent evaluation process
• Comparing models on actual work tasks
• Investigating a drop in evaluation scores

Helps design focused AI products built on APIs such as OpenAI and Anthropic. Covers product strategy, prompts, model selection, user experience, usage tracking, and cost management for tools aimed at solving specific customer problems.

• Planning an AI SaaS product for a specific problem
• Designing prompts for product features
• Balancing model choices, costs, and user experience

Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.

• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Let the host AI agent review Git changes while ocr selects files and resolves review rules. Receive findings grouped by severity, plus coverage details showing which files were reviewed or skipped and why.

• Reviewing changes with the current AI agent
• Checking branch differences or a single commit
• Reviewing files against their applicable rules

Plan Claude Code agent teams by choosing team size, roles, and agent types with the tools each task needs. Preset compositions cover reviews, debugging, implementation, research, security audits, and migrations, alongside display settings for local or CI environments.

• Choose team size and roles for a development task
• Assign separate review areas to multiple agents
• Split bug investigation across competing hypotheses

Create step-by-step code walkthroughs tailored to readers such as new contributors, reviewers, or bug fixers. Produce CodeTour .tour files linked to verified files and line numbers, with explanations of behavior, design choices, and pitfalls.

• Create a code walkthrough for new team members
• Guide reviewers through a PR and its risks
• Trace code involved in a bug or incident

Create and manage GitHub issues for the Kilo VS Code extension or JetBrains plugin using gh, GitHub's command-line tool. Guides template selection, clear titles, required project board assignment, and authentication permissions.

• Reporting a bug in the VS Code extension
• Requesting a feature for the JetBrains plugin
• Filing usage or design questions

Develop solution ideas through repeated rounds of research, cross-domain borrowing, recombination, and evaluation. Receive ranked candidates with feasibility and novelty scores, supporting and opposing arguments, and a record of where each idea came from and how it changed.

• Develop newsletter retention strategies
• Find approaches inspired by other fields
• Combine and refine existing feature ideas

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Deploy and operate Vercel projects from the command line. Manage environment variables and domains, investigate build failures with logs and metrics, and handle platform settings while checking which project and team each operation targets.

• Deploy preview and production versions
• Manage environment variables, domains, and DNS
• Investigate build failures and performance issues

Turn an uploaded video into three highlight edits with Adobe Quick Cut. Choose a target length and style, then compare versions that feature different moments and download the ones you prefer.

• Creating a short highlight video for social media
• Condensing interview footage into a highlight reel
• Comparing three edits with different moment selections

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

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