「composition」の検索結果

Use on every UI change - Atomic Design levels, reuse-before-build, correct placement, and import direction through the shared component library.

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。

Phase G of a WinForms-to-Avalonia migration: decompose oversized UI state and orchestration, harden asynchronous workflows and external boundaries, preserve composition lifetimes, keep Headless tests process-isolated, and update ownership documentation. Use only when an explicit migration has post-port structural or resilience debt, or when the orchestrator routes here. Baseline input security belongs in the phase that introduces the boundary, not only here.

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。

Phase D of a WinForms-to-Avalonia migration: build the Avalonia UI shell with composition, MVVM state and commands, responsive XAML, accessibility, localization boundaries, and behavior-focused unit/Headless tests. Use only when an explicit migration is ready to port Forms or when the orchestrator routes here. Prefer this over control-by-control ports.

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。

Inspect and diagnose minimal-zine posters, photo restyles, editorial images, and social-card series for subject preservation, factual and text integrity, composition, paper realism, print-process coherence, color signal, anti-AI craft, platform fit, and series continuity. Use when the user asks whether an image has高级感, feels AI-generated, matches Paper Signal or gc-minimal-zine quality, needs improvement, or should be regenerated. Returns evidence-based findings and a targeted correction prompt; regenerates only when the user also asks for a fix.

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。

Art-direct and generate one high-craft minimal-zine bitmap from a landscape, portrait, person, object, product, food, plant, building, interior, photograph, mood, sentence, or article concept. Use when the user wants a standalone poetic poster, editorial cover, visual concept, aesthetic photo upgrade, photo-to-zine restyle, or a subject-aware minimal-zine image without the overhead of a social carousel. Chooses adaptive composition and a physically coherent print process, preserves subject identity, saves the final prompt, invokes the runtime-native image generator, and audits the raster result.

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, write, art-direct, generate, and package a coherent 2-10 image minimal-zine social series from notes, articles, photographs, travel stories, products, reviews, cultural topics, data, PDFs, or mixed subjects. Use for Xiaohongshu/小红书图片, Instagram carousels, social visual essays, recommendation posts, photo diaries, evidence-backed explainers, or any multi-image editorial sequence. Produces analysis, card roles, subject-aware composition, saved prompts, real raster images, natural caption copy, evidence provenance when needed, and series-level QA.

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, art-direct, generate, edit, and quality-check tactile minimal-zine bitmap imagery from almost any subject: landscapes, portraits, objects, products, architecture, interiors, existing photographs, moods, article ideas, cultural essays, data, reviews, and social-media card series. Use for 小红书图片, Instagram carousels, poetic posters, editorial covers, photo restyling, visual essays, evidence-aware explainers, or any request for aged paper, photocopy, risograph, letterpress, sparse type, and restrained single-ink character. Routes the subject before choosing composition, preserves identity and factual content when editing, saves reproducible prompts, invokes the runtime-native image generator, and audits the real raster output. Do not use for glossy commercial ads, deterministic UI/vector assets, or exact diagram rendering.

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。

Decomposition methodology for pm agent — turns an approved ARCH document into a Beads task list with explicit dependencies, time-boxes, and acceptance criteria. The pipeline can only orchestrate work it can see; this skill defines what "seeable work" looks like.

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。

Designs high-performing ad creatives across Instagram, Facebook, TikTok, LinkedIn, Google (Display / Demand Gen), YouTube, Pinterest, and X/Twitter — locking platform dimensions, safe zones, text overlay policy, character limits, industry visual DNA, ad objective hierarchy, composition patterns, CTA strategy, and prompt sanitation. Use when the user says "create an ad", "design an ad creative", "make an ad", "广告创意", "帮我做个广告图", "Instagram ad", "TikTok ad", "Facebook ad", "Google Display ad", "LinkedIn ad", "YouTube ad", "Pinterest ad", "promotional image", "campaign creative", "ad banner", "投放图", or any platform-specific ad request. Routed from `product-shots` when `asset_type == "ad"` or `is_promotion == True`.

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。

Guides writing, reviewing, and reasoning about modern Android UI code using Jetpack Compose. Covers best practices for state management, side effects, recomposition, navigation, Material 3 design, accessibility, and performance. Use when reading, writing, or reviewing any Jetpack Compose project.

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。

AI agent architecture, graph and loop composition, protocol choice, evaluation, and observability. Use when scoping or reviewing systems before implementation.

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。

Apply @aleabitoreddit ("Serenity")'s distilled stock-analysis method to ANY ticker, sector, or situation — critical-chokepoint / supply-chain-OSINT idea discovery, first-principles value-chain decomposition, a Buffett-style quality gate (moat / profitability / customer-replacement risk, all default unverified), and narrative-vs-fundamentals hygiene. Produces a beginner-friendly Chinese analysis (她的观点 / 小白解释 / 第一性原理 / Buffett 直接判断 / 当前结论) that classifies an idea as 研究地图 vs 可投资结论. Trigger on "analyze like Serenity / 用 aleabito 的方法分析 / 用 Serenity 框架 / critical chokepoint 分析 / 第一性原理 + Buffett 判断这只股 / supply-chain bottleneck thesis". Never emits buy/sell calls.

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 design specification documents for Go programs before implementation begins. Use this skill when a user has a Go spec, design doc, RFC, or PRD to review, wants feedback on a planned feature in a Go codebase, is about to start implementing from a spec, or asks "is this spec ready?" or "review this before I build it". Applies Go philosophy — simplicity, composition, explicit errors, context propagation — plus Cobra/Viper CLI conventions where applicable.

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。

Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.

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。

Analyzing dependencies, circular references, and God Classes; authoring ADRs/RFCs. Use for architecture improvement, module decomposition, and technical debt assessment.

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。

Light Blender scenes professionally — three-point setups, HDRI environments, studio/cinematic/dramatic configurations, light groups, color temperature, soft vs hard shadows. Use whenever the user asks to "light the scene", "set up lighting", "make it look cinematic / dramatic / studio / outdoor / sunset", "add a key light", "use HDRI", or any lighting-related request. Make sure to use this skill even if the user does not say "light" — also covers "make it look professional", "studio shot", "moody atmosphere", "golden hour", "rim light". Pairs with blender-materials (lighting reveals materials) and blender-cameras (lighting + composition together = shot).

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。

Break down all competitor ASIN traffic sources under a given keyword -- organic search, SP ads, SB brand ads, SBV video ads, SP recommendations, AC/ER/TR recommendation slots, with support for ASIN filtering, custom date ranges, and new traffic keyword filters. Trigger when the user mentions keyword traffic sources, which competitors are grabbing traffic under this keyword, organic traffic vs paid traffic share, SP ad exposure, brand ad share, SP recommendation slots, recommendation ad/non-ad split, search display analysis, Amazon's Choice or editorial recommendation exposure, keyword competitive landscape, ASIN traffic composition, keyword traffic, traffic structure analysis, search share, ad share, traffic source distribution, Nexscope, traffic analysis, SP recommendation, recommend position breakdown. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing competitor ASIN traffic source distribution under a keyword, this skill should also be triggered.

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 Nexscope (Search Intelligence Framework) data to analyze ASIN traffic source composition and exposure distribution, covering current period/previous period/newly entered/exited period comparisons. Trigger when the user mentions ASIN traffic sources, traffic structure analysis, organic traffic vs paid traffic share, exposure score breakdown, period comparison, new/exited traffic keywords, competitor traffic analysis, SP ad keyword count, brand ad exposure, Amazon's Choice exposure, editorial recommendation exposure, Top Rated exposure, video ad exposure, organic search exposure ratio, PPC traffic sources, promotional deal traffic sources, recommendation slot structure breakdown, ASIN traffic analysis, traffic sources, organic traffic share, ad traffic share, exposure analysis, traffic structure, period-over-period comparison, keyword churn, Nexscope. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing an ASIN's traffic sources, exposure channel distribution, cross-period comparison, or competitor traffic structure comparison, this skill should also be triggered.

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。

Recommends team composition based on outcomes and mode. Invoked by launch commands during team setup.

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。

Structural pattern analysis for writing-mode review. Performs paragraph-level decomposition and pattern aggregation. Invoked by the team lead during editorial 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。

AI Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4 weeks reduced to one strategist + AI in 5 days. By Gingiris.

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。

Reviews shadcn/ui components for CVA patterns, composition with asChild, accessibility states, and data-slot usage. Use when reviewing React components using shadcn/ui, Radix primitives, or Tailwind styling.

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。

Reviews WidgetKit code for timeline management, view composition, configurable intents, and performance. Use when reviewing code with import WidgetKit, TimelineProvider, Widget protocol, or @main struct Widget.

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。

Reviews SwiftUI code for view composition, state management, performance, and accessibility. Use when reviewing .swift files containing SwiftUI views, property wrappers (@State, @Binding, @Observable), or UI code.

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。