2,635 件 ・ 関連度順
概要と使いどころ
"Optimize Core Web Vitals (LCP, INP, CLS) for better search rankings and user experience. Use this skill when the user needs to diagnose page speed issues, improve Largest Contentful Paint, reduce layout shift, or pass Google's page experience signals — even if they say 'my site is slow', 'Core Web Vitals failing', or 'page speed optimization'.".
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。
"Optimize advertising budget allocation across campaigns using marginal returns analysis. Use this skill when the user needs to distribute budget across multiple campaigns, optimize spend pacing, or maximize overall ROAS under budget constraints — even if they say 'how to split my ad budget', 'campaign budget optimization', or 'diminishing returns on ad spend'.".
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。
"Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.".
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。
Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.
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。
Performs a lightweight pre-release readiness review of Atlassian Forge apps across manifest/module wiring, architecture, runtime compatibility, dependency posture, tests, deploy readiness, and obvious security, cost, or reliability smells. Use when the user asks "review my Forge app", "pre-deploy check", "is this app ready to ship", "review manifest", "general app review", "release readiness", or asks for a broad quality pass. Do not use for deep security audits/SAST/exploitability review, cost optimization, or diagnosing a known broken app; route those to forge-security-review, forge-cost-optimizer, or forge-debugger respectively.
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。
Design rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.
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 building 2D games in Python with pygame - game loop and delta time, sprites and groups, collision detection, drawing and surfaces, events and input, sound and fonts, camera, or performance optimization
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 styling with Tailwind CSS v4 - @theme syntax, design token architecture, dark mode strategy, bundle size optimization, component-layer discipline, or migrating from v3
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 writing, reviewing, or refactoring Rust code. Covers ownership, borrowing, error handling, async patterns, API design, memory optimization, performance, testing, and common anti-patterns. Targets Rust 1.80+ and Edition 2024. Invoke with /rust-core.
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 building GBA games with the BPCore Lua engine - entity, sprite and tilemap functions, SRAM save and load, link cable multiplayer protocol, camera and scrolling, or optimization 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。
Use when building Django applications - security hardening, authentication and permissions, ORM optimization, PostgreSQL features, Django 6.0, migrations, testing, and ecosystem libraries
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。
Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.
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。
React Hook Form patterns - useForm, Controller, useFieldArray, validation resolver, performance optimization
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。
Bundle optimization, render performance, Core Web Vitals
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。
Background fetch, processing tasks, background location, headless JS, battery optimization - Expo and bare React Native
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。
React Native mobile development patterns - New Architecture (Fabric, TurboModules, JSI), component architecture, React Navigation 7+, FlashList v2 optimization, gestures with Reanimated 4, platform-specific code, React 19 features
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。
Query optimization, caching, indexing, connection pooling, async 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。
Tailor a resume to a job description with ATS keyword optimization, gap analysis, and rewritten bullets — zero fabrication. Use when the user wants /tailor for a single role, /batch for multiple JDs, career pivot reframing, or interview prep questions from a tailored application.
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。
Run email marketing end-to-end — list building, campaigns, automation flows, deliverability, and performance optimization.
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。
Align language models with human preferences using Direct Preference Optimization — no reward model, no RL loop.
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。
Create digest emails — curated summaries, personalization, frequency strategy, and engagement optimization.
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。
Increase conversion rates — CRO audits, A/B testing, landing page optimization, and experimentation programs.
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。
CircleCI guidance — config design, orbs, caching, workflows, contexts, and pipeline optimization.
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。
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.
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。