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VIP+W (View-Interactor-Presenter-Worker) Clean Architecture for iOS with unidirectional data flow and proper separation of concerns

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。

Expert Core Data guidance (iOS/macOS): stack setup, fetch requests & NSFetchedResultsController, saving/merge conflicts, threading & Swift Concurrency, batch operations & persistent history, migrations, performance, and NSPersistentCloudKitContainer/CloudKit sync.

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。

Pure UIKit patterns for state management, data binding, and component design without SwiftUI bridging

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。

Ground rules for an effective subagent in a loop, workflow, or graph run. Stay in scope, escalate when stuck, return structured results. Use when acting as a subagent, writing subagent prompts, or reviewing delegated agent behavior.

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。

Choose OpenRouter models and routers, query live catalog data, use structured outputs and batch inference, and debug routing with metadata. Use when working with OpenRouter APIs, openrouter/auto, openrouter/free, model discovery, pricing, providers, batch jobs, or structured JSON responses.

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。

Use this skill whenever user-controlled input flows into a MongoDB query, filter, or update. Trigger words: NoSQL Injection, ユーザー入力, req.body, クエリに渡す, find, filter, spread, $where, 検索条件, security review.

Use this skill when implementing full-text search, autocomplete, or vector/semantic search and RAG with MongoDB Atlas. Trigger words: 全文検索, あいまい検索, オートコンプリート, ベクトル検索, セマンティック検索, 類似検索, Atlas Search, $search, $vectorSearch, RAG, embedding.

Use this skill when validating API input, environment variables, form data, or DB responses at runtime boundaries. Trigger words: バリデーション, 入力検証, Zod, スキーマ, env 検証, 型強制, parse, safeParse, ランタイム境界.

This skill should be used when the user asks to "fill a template", "テンプレート入力", "書式入力", "裁判所書類の作成", "テンプレートに入力", "書式に記入", "この書式に入れて", "PDFからテンプレートに", "書類を作成して", "これも追加して", "このデータも入れて", or wants to auto-populate or incrementally add data to court documents.

Check, read, look up and generate the JSON of a Construct 3 folder project (project.c3proj, eventSheets, layouts, objectTypes, families) against the Construct3-RAG schemas and the rules the Construct 3 editor applies when it opens a project. Use this skill whenever you write or edit an event sheet or any other project file of a Construct 3 game, need the exact id, parameters and JSON of a condition, action or expression, want to read an event sheet or an official example as events instead of JSON, generate a whole project from a script, or the editor refuses to open or preview a project, even if the user only says "add a mechanic", "fix this event" or pastes an editor error.

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。

Build and verify Analytics visualization shell for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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。

Build and verify Analytics visualization shell for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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。

Build and verify Analytics visualization shell for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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。

Build and verify Analytics visualization shell for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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。

Build and verify Analytics visualization shell for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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。

Data viz design: production-level visual encodings, chart contrast, RTL axes. Use when implementing, reviewing or testing data viz design for React, Vite, Next.js or Perfect_AI projects.

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。

Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.

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。

Plan and execute Convex schema migrations safely, including adding fields, creating tables, and data transformations. Use when schema changes affect existing data.

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。

Use when the user says /localstop, localstop, stop localhost for this app, /localstopall, localstopall, or asks to stop local development servers for this Teleprompt app.

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。

Set up and run Direct Preference Optimization (DPO) training on preference datasets using the Tinker API. Use when the user wants to train with preference data, chosen/rejected pairs, or DPO.

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。

Set up and run multi-turn RL training for interactive environments (terminal tasks, tool use, search/RAG, games) using the Tinker API. Use when the user wants multi-turn RL, agentic training, tool-use RL, or interactive environment training.

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。

Reference for Tinker SDK types — Datum, ModelInput, TensorData, SamplingParams, response types, error types, and helper functions. Use when the user needs to build training data, construct model inputs, understand response objects, or handle errors.

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。

Guide for defining RL environments — the Env protocol, EnvGroupBuilder, RLDataset, and custom environment creation. Use when the user asks about RL environments, reward functions, or how to define custom tasks for RL training.

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。

Set up and run supervised fine-tuning (SFT) on instruction or chat datasets using the Tinker API. Use when the user wants to do instruction tuning, chat fine-tuning, or supervised learning.

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。