「men」の検索結果

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。

Complete workflow for dynamic analysis. Use when user mentions impact, crash, drop test, transient, or time-varying response. Handles explicit and implicit dynamics.

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 Tosca optimization. Use when user mentions design response, objective function, optimization constraint, or SIMP penalty. Base module for topology/shape 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。

Agent-to-Agent (A2A) server configuration patterns for HTTP, STDIO, SSE, and WebSocket transports. Use when building A2A servers, configuring MCP transports, setting up server endpoints, or when user mentions A2A configuration, server transport, MCP server setup, or agent communication protocols.

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。

Define time-varying amplitudes. Use when user mentions ramp, time-varying, cyclic, pulse, or gradually increasing loads. Does NOT handle static constant loads.

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 static structural analysis. Use when analyzing stress, displacement, or reaction forces under constant loads. For strength and stiffness 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。

Analyze multi-body contact. Use when user mentions parts touching, friction between surfaces, bolt-plate contact, press fit, or assembly with contact.

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 designing experiments for subject lines, offers, cadences, or journeys.

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。

4claw — a moderated imageboard for AI agents. Boards, threads, replies, media uploads, bumping (bump=false to not bump), greentext, and automatic capacity purges. Post spicy hot takes (what you're REALLY thinking) — but no illegal content, doxxing, harassment, or minors.

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 working in Blender, Unity 3D, Unreal Engine, Cinema 4D, VR/AR applications, or any three-dimensional animation 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。

[62] VALIDATE. Final self-check before delivery. Verify goal alignment, completeness, correctness, and identify residual risks. Produces quality score (0-100) and delivery status. Use when completing any significant work, before handoff, or when you need confidence that work is ready.

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。

ALWAYS use this skill when user needs ANY API functionality (AI models, image generation, video, audio, text processing, etc.). Automatically search 302.AI's 1400+ APIs and generate integration code. Use proactively whenever APIs or AI capabilities are 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。

2D game development principles. Sprites, tilemaps, physics, camera.

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 timeless programming wisdom from "97 Things Every Programmer Should Know" when writing, reviewing, or refactoring code. Use for design decisions, code quality checks, professional development guidance, testing strategies, and workflow 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。

Deep knowledge expert for the 33GOD agentic pipeline system, understands component relationships and suggests feature implementations based on actual codebase state

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。

[52] EXECUTE. Three-stage refactoring workflow: (1) iterative research of refactor/modularization options, (2) plan + risk/edge-case analysis + Scope150 validation, then implement with tests after user confirmation, and (3) apply Scout105 cleanup protocol. Use when asked to refactor, modularize, or restructure code safely.

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。

3D game development principles. Rendering, shaders, physics, cameras.

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 and analyze A/B tests, calculate statistical significance, and determine sample sizes for conversion optimization and experiment validation

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。

9D product development framework

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3D web graphics with Three.js (WebGL/WebGPU). Capabilities: scenes, cameras, geometries, materials, lights, animations, model loading (GLTF/FBX), PBR materials, shadows, post-processing (bloom, SSAO, SSR), custom shaders, instancing, LOD, physics, VR/XR. Actions: create, build, animate, render 3D scenes/models. Keywords: Three.js, WebGL, WebGPU, 3D graphics, scene, camera, geometry, material, light, animation, GLTF, FBX, OrbitControls, PBR, shadow mapping, post-processing, bloom, SSAO, shader, instancing, LOD, WebXR, VR, AR, product configurator, data visualization, architectural walkthrough, interactive 3D, canvas. Use when: creating 3D visualizations, building WebGL/WebGPU apps, loading 3D models, adding animations, implementing VR/XR, creating interactive graphics, building product configurators.

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。