143 件 ・ 関連度順
概要と使いどころ
Lay out and organize a ComfyUI workflow cleanly on the live panel canvas. Dependency-layered node placement with no overlaps, subgraphs, colored group boxes, and subgraph rail alignment. Use when asked to tidy / clean up / organize / arrange a workflow, add groups or subgraphs, fix overlapping nodes, or build a workflow that should look good from the start.
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。
Generates an app icon for macOS or iOS — a fast CoreGraphics placeholder and/or flat layered source art to finish in Icon Composer (the Liquid Glass / iOS 26+ standard). Produces appearance variants (light/dark/tinted) and installs into the asset catalog. Use when the user wants to create, generate, iterate, or update an app icon.
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 mandates for acceptance tests - hexagonal boundary, business language abstraction, user journey completeness, pure function extraction, 3 Pillars (domain language / chained narrative / production composition), and the layered ATD discipline (Universe-bound assertion, layer-dependent PBT mode, two-tier acceptance, example-based sad paths)
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 needs to understand customers deeply enough to write copy and pick targeting — consumer versus shopper, JTBD, layered persona, internal monologue, pain map, objection list, journey map, and a customer language bank pulled from real reviews and DMs. Trigger on 'customer insight', 'buyer persona', 'who is my customer', 'what do they actually want', 'jobs to be done', 'why do they hesitate before buying'. Also use when the user cannot explain who the product is for. Not for — intake questions when an agency signs a new client, see `20-client-intake-brief-global`; paid audience targeting setup, see `51-audience-research-global`; competitor study, see `08-competitor-research-global`.
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 marketing numbers look bad and the user needs to know WHY — layered root-cause diagnosis across tracking, delivery, creative, landing page, offer, and audience quality, 5-Whys, regional benchmarks for US, EU, SEA, and LATAM, and a 48-hour action plan. Covers dropshipping ROAS and BE-ROAS. Trigger on 'why is CPA so high', 'my ads are not converting', 'ROAS dropped', 'we are burning budget', 'performance is bad this month', 'diagnose the funnel'. Also use when the user pastes a screenshot of numbers with no question attached. Not for — auditing account configuration and scoring health, see `21-ads-audit-global`; writing a report someone else reads, see `07-marketing-report-global`; turning a raw export into insight, see `13-data-analysis-global`.
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。
把一张或多张图片整理成 PSD 图层文件的创作与转换 skill。当用户需要 image2psd、图片转 PSD、 多张图片拼成 PSD、海报/设计稿拆成多个图层、白底转透明、颜色聚类拆层、把 Codex/AI 生图结果拆成元素图再合成 PSD、 或希望输出 layered PSD、可在 Photoshop/Photopea 中编辑的分层栅格文件时，应该使用此 skill。 在 Codex 中使用时，默认结合 imagegen skill：先用 Codex 视觉/生图能力理解、补齐或重建元素，再用本 skill 的脚本落地 PSD。
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。
Cross-cutting standards for error design, response shapes, error codes, and boundary placement across API, domain, and infrastructure layers. Use when defining error hierarchies, wrapping exceptions, building standardized error responses, or placing error boundaries in layered 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。
This skill should be used when a Ralph phase must identify critical user decisions, run a layered grill, persist partial answers, obtain explicit approval, or resume an interrupted phase interview before delegating artifact 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。
Generate interactive 3D holographic collectible-card websites from a user description or reference image, using layered artwork, Blender and Three.js. Includes project-local Blender installation, reusable parallax materials and browser verification. Host-agnostic — any harness that runs a multimodal model can use it, since the model draws the layer artwork and inspects the rendered frames. Use when the user asks for 全息闪卡, 镭射卡, 3D 卡牌网页, a holographic collectible card site, or an editable card.blend.
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。
Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend 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。
Progressive memory recall with 4 scope layers AND 3 depth layers. Scope: identity > project > room > deep. Depth: IDs only > summary > full. 10-50x token savings through fetch-on-confirmation pattern.
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。
Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.
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。
Trigger for: FFP IGCE, firm-fixed-price estimate, FFP cost model, proposed FFP rate validation, wrap-rate analysis, Agency BPA rate comparison, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable Firm-Fixed-Price federal estimates using BLS OEWS wages, layered fringe/overhead/G&A/profit, GSA CALC+ positioning, and GSA Per Diem travel. Use for FFP-by-period, FFP-by-deliverable, SOW/PWS decomposition, implied-multiplier analysis, and fixed-price scenario comparisons. Do NOT use for Labor Hour, T&M, or cost-reimbursement IGCEs (use IGCE Builder LH/T&M or IGCE Builder CR). Do NOT use for grant budgets. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.
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。
Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.
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。
Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).
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。
Bringing up a complete ROS2 system on a robot's onboard computer: systemd services, launch file composition, ordered startup, and production monitoring. Use this skill when configuring a robot to start ROS2 nodes on boot, writing systemd unit files for ROS2 launch, composing layered launch files for full robot stacks, setting up watchdog monitoring, configuring udev rules for deterministic device naming, or debugging boot-time race conditions. Trigger whenever the user mentions robot bringup, robot startup, systemd for ROS2, ROS2 on boot, launch file composition, robot boot sequence, udev rules for cameras or serial ports, automatic restart for ROS2 nodes, network configuration for multi-machine ROS2, log rotation for robots, graceful shutdown of robot stacks, or SSH-based remote debugging. Also trigger for sourcing workspaces in systemd, ordered startup with health checks, or running ROS2 systems as long-running production services. Covers systemd on Ubuntu 22.04/24.04 with ROS2 Humble, Iron, and Jazzy.
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。
Generates comprehensive TypeScript documentation using JSDoc, TypeDoc, and multi-layered documentation patterns for different audiences. Use when creating API documentation, architectural decision records (ADRs), code examples, and framework-specific patterns for NestJS, Express, React, Angular, and Vue.
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 and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend 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。
Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Java 21+ Spring Boot 3.5+ applications. Use when structuring layered architectures, separating domain logic from frameworks, implementing ports and adapters, creating entities/value objects/aggregates, or refactoring monolithic codebases for testability and maintainability.
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。
Configure agy-auto PreToolUse security gate to run Antigravity CLI (agy) unattended with layered policy controls instead of --dangerously-skip-permissions.
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 packaging a Spring Boot 3 application as an OCI image or GraalVM native executable. Covers buildpacks, layered images, JVM containers, AOT hints, probes, security, and 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。
Use when generating or modifying any Spring Boot class — controllers, services, repositories, DTOs, mappers, or configuration. Enforces strict layer separation and prevents business logic from leaking across boundaries.
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。
Scene composition and orchestration skill for creating complex multi-part animations. **Triggers when:** - User wants to compose multi-element scenes - Request involves combining multiple visual elements - Scene requires act-based structure or transitions - User mentions "scene", "compose", "combine", "orchestrate" **Capabilities:** - Scene graph construction with acts and transitions - Multi-object coordination and timing - Camera movements and focus control - Layered animation sequences
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。
Implements GDPR Article 12 transparent information and communication requirements, covering concise, intelligible, and plain language obligations, response timelines, fee and refusal provisions, and layered notice design. Activate for transparent communication, Art. 12, privacy notice, plain language, response timeline queries.
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。