「red」の検索結果

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

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 iOS App Clips with invocation URLs, NFC, QR codes, App Clip Codes, Safari banners, Maps, Messages, and shared App Group data handoff to the full app. Covers target setup, experience configuration, size limits, NSUserActivity handling, SKOverlay promotion, location confirmation, and capability limits. Use when creating App Clips or wiring App Clip invocation and full-app migration.

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。

Add Apple Pencil drawing, canvas views, tool pickers, and ink serialization using PencilKit. Use when building drawing apps, annotation features, handwriting capture, signature fields, or any Apple Pencil-powered experience on iOS/iPadOS/visionOS.

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。

Prepare for App Store review and prevent rejections. Covers App Store review guidelines, app rejection reasons, PrivacyInfo.xcprivacy privacy manifest requirements, required API reason codes, in-app purchase IAP and StoreKit rules, App Store Guidelines compliance, ATT App Tracking Transparency, EU DMA Digital Markets Act, HIG compliance checklist, app submission preparation, review preparation, metadata requirements, entitlements, widgets, and Live Activities review rules. Use when preparing for App Store submission, fixing rejection reasons, auditing privacy manifests, implementing ATT consent flow, configuring StoreKit IAP, or checking HIG compliance.

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。

Analyze and optimize React component performance issues (slow renders, re-render thrash, laggy lists, expensive computations). Use when asked to profile or improve a React component, reduce re-renders, or speed up UI updates in React apps.

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。

Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization patterns.

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。

Strips AI fingerprints from written text by enforcing banned vocabulary, forbidden sentence structures, and authentic prose patterns. Use when writing articles, documentation, social content, emails, or any text that should read as human-authored rather than AI-generated.

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。

Devil's-advocate attack on AI workflow designs — threat model, assumption challenges, code critiques, and hypothetical redesigns with no file edits. Use for "attack this design", devil's advocate, or hostile challenge on agents/tools/summarization/tokens. Not for PR peer review (code-review) or self-review that applies fixes (ai-systems-review). Prefer: this skill → decide → ai-systems-review.

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。

Disables the AI's default hedging, diplomatic softening, and social-nicety layers to deliver cold, unfiltered, consequentialist analysis on any topic. Use when the user wants the AI's actual assessment without padding, regardless of how harsh or uncomfortable the honest answer is.

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。

Structured 3-phase peer review of a diff or PR — intent, issues by severity, then pointed merge-readiness questions and a verdict. Use for general code review. Not for AI workflow self-fix-and-edit (ai-systems-review) or no-edit devil's-advocate threat models (adversarial-challenge). Phase 3 asks questions only; it is not the adversarial-challenge skill.

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。

Analyze a job description, hiring post, or recruitment page for role requirements, evaluation criteria, keywords, risks, and resume or cover letter angles. Use when the user provides a JD URL/text, asks what a company is looking for, wants JD-resume gap analysis, or needs inputs for a tailored cover letter. 한국어 요청도 포함합니다 - 채용공고 분석, 공고 분석, JD 분석, 직무기술서 분석, 자격요건, 우대사항, 이 공고 뭘 원하는지, 공고랑 내 경력 비교.

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。

Write Korean job application cover letter drafts from a question, JD, resume.md, and optional company values input. Use when the user asks for 자기소개서, 자소서, 지원동기, 입사 후 포부, 필살기, 3C4P experience decomposition, experience-based application answers, or tailored cover letter text grounded in resume.md evidence.

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。

This skill should be used when the user asks to "analyze a lawsuit", "訴訟分析", "事件分析", "書面分析", "case analysis", "判決分析", "訴訟書類の整理", or wants to extract structured information from litigation documents.

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。

Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.

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。

Manual testing + bug filing for Enterprise / SaaS products. Parses a Feishu test doc (scope + repo + bitable URL), observes staging via claude-in-chrome, captures DOM/console/network/screenshot evidence, then dual-tracks each finding — Feishu Bitable record (with attachments) + GitHub issue (<REPO>#<ISSUE_NUMBER> format — English-primary, Chinese collapsed, R2-hosted screenshots, matching type label). Enforces dedup across bitable + GitHub issues + PRs, and mandatory P0/P1/P2 priority prompt when unclear. Trigger whenever the user says "test and report", "test and report side-by-side", "start system testing", "experience staging", "collaborate on bug filing", "collect feedback", a shared Feishu testing doc, a bitable URL + finding, or any UX/bug needing both bitable + GitHub tracking. Do NOT use for writing product code, PR review, CI debugging, or general "how do I use this product" questions — those go to normal dev / PR-review / support flows.

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。

MUST USE whenever the user wants to automate a real desktop or sandbox - clicking, typing, scrolling, screenshotting, running an OS shell command, or handing a high-level 'open browser and do X' task to an autonomous computer-use agent. Wraps the trycua/cua Python toolkit via its `cua` CLI - pynput-based, cross-platform (macOS / Linux / Windows). NO custom tools are registered; you call `cua` through pi's built-in bash and read screenshots back through the Read tool. Triggers: cua, computer use, computer-use, GUI automation, screenshot the desktop, click on the screen, type into the active app, scroll the page, control my computer, drive my browser, sandbox, docker sandbox, QEMU sandbox, Lume sandbox, ComputerAgent, cua do, cua sandbox, 컴퓨터 자동화, 스크린샷 찍어, 내 컴퓨터 조작, 브라우저 열어서, 샌드박스, 화면 자동화, 마우스로 클릭, 키보드 타이핑, computer use 위임, 자동으로 클릭, 자율 에이전트로 처리.

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。

Review Replica pull requests using the same shared conventions and project guides as implementation agents. Use when reviewing changes to code, tests, workflows, or documentation in this repository.

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。

Shared context template library for setup-skills. Read when discovering context documents or their ownership. This library supplies research inputs, not active product context or a separate authoring workflow.

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。

Creates or updates believable deterministic raw fixture scenarios without breaking an existing prototype. Use when asked to add or change fixtures, seed data, sample data, scenarios, or cross-record relationships. Not for UI redesign, live-data infrastructure, or unrelated commands.

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。

پرداخت امن assistant for Codex; apply when building Persian/RTL web products, localizing content or integrating the related Iranian workflow. Local independent adaptation inspired by publicly documented VibeFarsi topics.

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。

Authoring Codex Skills: production-level frontmatter, triggered scope, runnable assets. Use when implementing, reviewing or testing authoring codex skills 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。

Test-driven development: production-level red green refactor and boundaries. Use when implementing, reviewing or testing test-driven development 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。

نگارش فارسی assistant for Codex; apply when building Persian/RTL web products, localizing content or integrating the related Iranian workflow. Local independent adaptation inspired by publicly documented VibeFarsi topics.

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。