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概要と使いどころ
Senior System Architect for SaaS specifications. Security-first design with ISO 27001 compliance. Use for system architecture, tech specs, API design, technology choices, distributed systems, cloud infrastructure.
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, revise, and validate publication-ready academic paper figures and tables. Use for LaTeX tables, related-work comparison tables, result tables, notation/dataset/taxonomy tables, precise source-data-driven experiment plots from CSV/JSON/logs, generated conceptual figures such as system overviews/pipelines/architectures/threat models, captions, artifact specs, source-data traceability, and paper-ready PDF/SVG/PNG/LaTeX exports. Do not use for prose-only paper writing, self-review, reviewer response, rebuttal drafting, or external literature-management workflows.
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 an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.
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。
Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.
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。
Reusable skill for coding agents (Claude, GPT, etc.) to build, optimize, debug, and productionize AI agents and coding agents in Rust. Embeds Rig/ADK patterns, Tokio async architecture, tracing observability, performance techniques, erro...
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 wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
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。
Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.
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 reactive, type-safe, production-grade backends. ALWAYS use this skill when the user asks to build, plan, design, or implement backend features, APIs, data models, server logic, database schemas, web apps, full stack apps, or mobile apps. This includes planning and architecture discussions.
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。
Project workflow for PRDs, task tracking, changelog sync, and documentation updates. Use for any non-trivial task that spans multiple steps, touches several files, changes architecture, or needs project tracking updates. Also activates with @update to sync task.md, changelog.md, and files.md after completing work.
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 convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.
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。
Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.
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。
Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.
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 convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.
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。
Project workflow for PRDs, task tracking, changelog sync, and documentation updates. Use for any non-trivial task that spans multiple steps, touches several files, changes architecture, or needs project tracking updates. Also activates with @update to sync task.md, changelog.md, and files.md after completing work.
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 reactive, type-safe, production-grade backends. ALWAYS use this skill when the user asks to build, plan, design, or implement backend features, APIs, data models, server logic, database schemas, web apps, full stack apps, or mobile apps. This includes planning and architecture discussions.
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。
Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.
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 Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, and cost 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。
Build content-focused websites with Astro — zero JS by default, islands architecture, multi-framework components, and Markdown/MDX support.
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。
AWS development with infrastructure automation and cloud architecture 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。
Build a new API connector or provider by matching the target repo's existing integration pattern exactly. Use when adding one more integration without inventing a second architecture.
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。
Production-grade Android app development guide covering native (Kotlin/Java), cross-platform (Flutter, RN, KMM), and hybrid architectures.
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。
Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.
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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered 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。
エージェントおよび LLM アプリケーション向けのフルスタック診断。12 層のエージェントスタックにおけるラッパーリグレッション、メモリ汚染、ツール規律の失敗、隠れた修復ループ、レンダリング破損を監査します。重要度順の発見事項とコードファーストの修正を生成します。エージェントアプリケーション、自律ループ、または LLM を活用した機能を構築する開発者に必須です。