データ・AIのスキル

Detect and recover text that was accidentally typed with the Taiwanese Bopomofo / Zhuyin (注音) keyboard while the input method (IME) was stuck in English/ASCII mode — what Taiwanese users call 注音亂碼 or 注音文 — producing meaningless-looking letter+digit sequences such as "su3cl3a8", "cl3", "g4", "2k7", "vu,4", or such garbled runs embedded in an otherwise normal request (e.g. "help me fix su3cl3a8", "看看這個 PR qk4ru.6", "幫我改 su3cl3a8xk7"). Use this skill whenever a user message contains short ASCII tokens that look like keyboard gibberish, do not form real English words, and cannot be understood normally — especially tokens with digits interspersed among letters (tone keys 3/4/6/7 or vowel keys 8/9/0), which are a strong sign of Mandarin phonetic keystrokes typed in the wrong input mode. Run scripts/decode_bopomofo.py to deterministically map the keystrokes back to 注音 symbols with a confidence score, then interpret the intended Traditional Chinese in context and continue the user's original request without making them retype. Do NOT trigger on genuine technical strings — code, file paths, URLs, git hashes, CLI flags, package names, version numbers — the script's exclusion rules and confidence tiers already filter these out. Especially valuable for Taiwanese developers who forget to switch input methods when prompting AI coding agents.

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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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Use the ax CLI instead of curl + throwaway parsing scripts whenever you fetch a URL, explore an unknown web page, or extract structured data from HTML. Trigger whenever you are about to write an inline script (python3 heredoc, node -e, regex over HTML) or a bare curl for one-off web fetching, scraping, or page exploration.

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Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.

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Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

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Query and maintain the user's local LLM Wiki from any Codex or Claude Code conversation or project. Use when the user asks to search personal knowledge, recall prior research or decisions, answer from the wiki, create durable notes, ingest a source into the wiki, update topic/concept/entity/question pages, write back useful answers, or run wiki lint/reindex workflows.

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Own MSBuild generated-artifact integration. USE FOR: a target that creates or should create source, Content, None, or another physical file but the artifact is missing from compilation or output; target timing; evaluation-time glob misses; $(IntermediateOutputPath) placement; and FileWrites clean tracking. The prompt may describe the generated artifact without naming the producing task. DO NOT USE when the primary defect is general Include/Remove/Update semantics, item metadata or batching, duplicate/overlapping declarations, or a generated-item identity relationship; use item-management. Exclude Roslyn source-generator internals and non-MSBuild systems.

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Guide developers through capturing diagnostic artifacts to diagnose production .NET performance issues. Use when the user needs help choosing diagnostic tools, collecting performance data, or understanding tool trade-offs across different environments (Windows/Linux, .NET Framework/modern .NET, container/non-container).

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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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Review and optimize application data access that uses Entity Framework Core (EF Core). Use for EF Core performance and correctness reviews of services, repositories, imports, and background jobs: slow or memory-heavy queries, excessive round-trips, hidden SaveChanges, premature materialization, dynamic query construction, result-graph tracking, bulk writes, DbContext lifetime or overlapping operations, search semantics, paging, raw SQL, and model/index coverage. Use whether or not EF Core owns the schema. For EF Core, not Dapper or raw ADO.NET.

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Generate or scaffold ASP.NET Core code — Razor Pages, Blazor components, MVC controllers, views, and Minimal API endpoints — without using ASP.NET Core CLI scaffolding/code-generation tools. Use when (1) adding CRUD pages, views, or API endpoints backed by Entity Framework (EF Core) and a database, (2) generating code to create, read, update, and delete data using a DbContext, (3) scaffolding UI components that match the project's existing CSS framework and coding patterns, or (4) creating data-driven forms, tables, and navigation for a model class. Do not use for non-ASP.NET projects or when CLI-based scaffolding is preferred.

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Guidance for implementing CollectionView in .NET MAUI apps — data display, layouts (list & grid), selection, grouping, scrolling, empty views, templates, incremental loading, swipe actions, and pull-to-refresh. USE FOR: "CollectionView", "list view", "grid layout", "data template", "item template", "grouping", "pull to refresh", "incremental loading", "swipe actions", "empty view", "selection mode", "scroll to item", displaying scrollable data, replacing ListView. DO NOT USE FOR: simple static layouts without scrollable data (use Grid or StackLayout), map pin lists (use Microsoft.Maui.Controls.Maps), table-based data entry forms, non-MAUI list controls, CarouselView or BindableLayout questions, platform-specific handler or renderer customization, diagnosing CollectionView bugs in the MAUI framework itself, or general MVVM/binding questions that merely happen to mention a list (use maui-data-binding).

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Create or review Blazor components (.razor files) with correct architecture. USE FOR: writing new Blazor components that do NOT involve JavaScript interop, implementing parameters and EventCallback, RenderFragment slots, component lifecycle (OnInitializedAsync, OnParametersSet), async patterns, IAsyncDisposable, CancellationToken, CSS isolation, code-behind. DO NOT USE FOR: creating new projects (use create-blazor-project), JavaScript interop or calling browser APIs from Blazor (use use-js-interop), forms and validation (use collect-user-input), prerendering issues (use support-prerendering), HTTP data fetching patterns (use fetch-and-send-data), coordinating state between unrelated components (use coordinate-components).

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Guide for implementing Shell-based navigation in .NET MAUI apps. Covers AppShell setup, visual hierarchy (FlyoutItem, TabBar, Tab, ShellContent), URI-based navigation with GoToAsync, route registration, query parameters, back navigation, flyout and tab configuration, navigation events, and navigation guards. Use when: setting up Shell navigation, adding tabs or flyout menus, navigating between pages with GoToAsync, passing parameters between pages, registering routes, customizing back button behavior, or guarding navigation with confirmation dialogs. Do not use for: deep linking from external URLs (see .NET MAUI deep linking documentation), data binding on pages (use maui-data-binding), dependency injection setup (use maui-dependency-injection), or NavigationPage-only apps that don't use Shell.

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Build forms, validate data, and react to user input in Blazor. USE FOR adding forms, search boxes, filter panels, inline editing, data-entry UI, file uploads, validation (annotations or custom), handling form submissions, and binding input controls. Covers EditForm, built-in input components, DataAnnotationsValidator, custom validation, SSR form patterns (SupplyParameterFromForm, FormName, AntiforgeryToken, Enhance), and @bind for simple interactive controls. DO NOT USE for project scaffolding (see create-blazor-project) or prerendering issues (see support-prerendering).

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Guidance for .NET MAUI XAML and C# data bindings — compiled bindings, INotifyPropertyChanged / ObservableObject, value converters, binding modes, multi-binding, relative bindings, fallbacks, and MVVM best practices. USE FOR: setting up compiled bindings with x:DataType, implementing INotifyPropertyChanged or CommunityToolkit ObservableObject, creating IValueConverter / IMultiValueConverter, choosing binding modes, configuring BindingContext, relative bindings, binding fallbacks, StringFormat, code-behind SetBinding with lambdas, and enforcing XC0022/XC0025 warnings. DO NOT USE FOR: CollectionView item templates and layouts (use maui-collectionview), Shell navigation data passing (use maui-shell-navigation), dependency injection (use maui-dependency-injection), or animations triggered by property changes (use .NET MAUI animation APIs).

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Share state between components that don't have a direct parent-child parameter relationship, using cascading values, scoped services with change events, or CascadingValueSource via DI. USE WHEN the user needs a CascadingParameter or CascadingValue that works across render mode boundaries, a shopping cart or notification count accessible from multiple pages, a theme or user preference cascaded app-wide, or when components in different parts of the tree must react when shared data changes. Also USE WHEN cascading values aren't reaching interactive children in per-page interactivity mode, or when the user needs to understand scoped vs singleton service lifetime for state on Blazor Server. DO NOT USE for direct parent-child parameter passing or EventCallback (see author-component), for persisting state across prerender-to-interactive transitions (see support-prerendering), or for service abstractions for data fetching in Auto/WebAssembly (see fetch-and-send-data).

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Call APIs, load data into components, and handle the async lifecycle in Blazor. USE FOR fetching data from a backend, submitting data to an API, displaying loading/error states, registering HttpClient, building service abstractions for Auto/WebAssembly render modes. DO NOT USE for form validation (see collect-user-input), prerendering persistence (see support-prerendering), or project scaffolding (see create-blazor-project).

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Make interactive Blazor components work correctly with prerendering. USE FOR fixing duplicate data loads, UI flicker during prerender-to-interactive handoff, null references during prerender, persisting state across prerender, disabling prerendering, excluding pages from interactive routing, or detecting whether a component is currently prerendering. DO NOT USE for choosing which render mode to use (see create-blazor-project) or general component authoring (see author-component).

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Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.

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Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.

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Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.

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Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.

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Systematic investigation of metric changes and anomalies. Use when a metric unexpectedly changes, investigating business metric drops, explaining performance variations, or drilling into aggregated metric drivers.

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。