2.1万 件 ・ 関連度順
概要と使いどころ
Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps. Invoke when user mentions abstraction levels, making concepts concrete, or explaining at different depths.
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。
Toolkit for creating an entertainingly stupid README that manages to give off Tim & Eric, Steve Brule vibes while maintaining a level of technical acumen that is expected of a modern open source project. This skill applies when users request a silly or absurd README.
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。
Abductive analysis for qualitative interview data following Timmermans & Tavory. Guides you through theory-first analysis that recognizes anomalies and generates novel theoretical insights through systematic puzzle exploration.
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 and manipulate Abaqus geometry - parts, sketches, extrusions, CAD import. Use for any geometry creation task including box, cylinder, or STEP/IGES import.
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。
Define material properties for FEA models. Use when user mentions steel, aluminum, Young's modulus, elastic, plastic, density, or asks about material properties.
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。
Export Abaqus geometry and results. Use when user mentions exporting to STL, STEP, CSV, or generating input files for external use.
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。
Complete workflow for heat transfer analysis - steady-state and transient thermal. Use when user asks about temperature distribution, conduction, convection, or heat flow.
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。
Agent-to-Agent (A2A) protocol implementation patterns for Google ADK - exposing agents via A2A, consuming external agents, multi-agent communication, and protocol configuration. Use when building multi-agent systems, implementing A2A protocol, exposing agents as services, consuming remote agents, configuring agent cards, or when user mentions A2A, agent-to-agent, multi-agent collaboration, remote agents, or agent orchestration.
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。
Integration patterns for combining Agent-to-Agent (A2A) Protocol with Model Context Protocol (MCP) for hybrid agent communication. Use when building systems that need both agent-to-agent communication and agent-to-tool integration, implementing composite architectures, or when user mentions A2A+MCP integration, hybrid protocols, or multi-agent tool access.
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。
Master skill for Abaqus FEA scripting. Use for any finite element analysis, topology optimization, or Abaqus Python scripting task. Routes to appropriate specialized skills.
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 China A-share stock analysis covering fundamental analysis, technical analysis, policy impact assessment, and market-specific features (T+1 trading, price limits, northbound capital flow). Use when user asks about A股分析, Chinese mainland stocks, Shanghai/Shenzhen listed stocks, or needs analysis considering China market characteristics.
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。
Fourth step in building instruction context for codebase
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 WCAG accessibility auditing with multi-tool testing (axe-core + pa11y + Lighthouse), TRUE PARALLEL execution with Promise.allSettled, graceful degradation, retry with backoff, context-aware remediation, learning integration, and video accessibility. Uses 3-tier browser cascade: Vibium → agent-browser → Playwright+Stealth.
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。
Complete Three.js building system with spatial indexing, structural physics, and multiplayer networking. Use for survival/crafting games, sandbox games, multiplayer construction, or any 3D building mechanics.
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。
5W1H Decision Framework Tool. Use for: (1) Systematic decision-making before creating todos, (2) Preventing duplicate implementation, (3) Detecting avoidance behavior, (4) Ensuring agile refactor compliance with executor/dispatcher separation
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。
[31] ANALYZE. Universal deep thinking methodology for any situation requiring quality reasoning. Use when solving problems, debugging, making decisions, analyzing code, planning, reviewing, or anytime you need thorough thinking instead of surface-level responses. Triggers on "think deeply", "analyze thoroughly", "reason carefully", "deep thinking", "understand completely", or any task requiring careful thought.
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。
Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.
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。
[70] CLOSE. Manage task lifecycle with correct statuses and no duplicates. Ensure each task has one status, artifacts match claims, and no task appears in multiple places. Use when managing task lists, updating progress, or ensuring task tracking integrity across systems.
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。
Expert 3D modeling specialist with deep knowledge of topology, UV mapping, game-ready and film-ready pipelines, DCC tool workflows (Blender, Maya, ZBrush, 3ds Max, Houdini), retopology, LOD systems, and export pipelines. This skill represents years of production experience distilled into actionable guidance. Use when "3d model, 3d modeling, mesh topology, uv unwrap, uv mapping, retopology, retopo, low poly, high poly, subdivision, subdiv, edge flow, edge loops, polygon modeling, box modeling, hard surface, organic modeling, sculpting, zbrush, blender modeling, maya modeling, 3ds max, LOD, level of detail, game ready mesh, film ready, baking normals, high to low, fbx export, gltf export, texel density, 3d, modeling, topology, uv, game-dev, vfx, blender, maya, zbrush, retopology, lod, hard-surface, organic, sculpting" mentioned.
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。
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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。
Fifth step in building instruction context for codebase
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。
Final step in building instruction context for codebase
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。
Third step in building instruction context for codebase
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。
[23] UNDERSTAND. Consult external AI models when internal sources are exhausted. Build quality prompts using Prompt150 formula (Context + Query + Method + Style). Use when Loop150 exhausts internal sources, need real-world precedents, confidence <75%, or require reasoning from specialized AI models.
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。