「character」の検索結果

Use when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip. Keywords: Meshy 7 and Meshy 6, Ultra mode, Smart Topology, PBR maps, texture prompt, triangle or quad topology, game-ready mesh, A-pose, T-pose, GLB pipeline.

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 generated assets must keep a consistent style, character, or product look and references stop scaling, or when a user asks to train a custom model through the Scenario MCP, fine-tune a LoRA, clone a voice, curate a training dataset, choose a base model, set epochs and sample prompts, estimate training cost, diagnose a trained model that lost its identity or has one epoch, or generate with a trained model. Keywords: custom model, LoRA, dataset curation, epoch previews.

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 3D assets with Rodin Hyper3D models on Scenario via MCP: image-to-3D from up to five multiview stills, text-to-3D from a prompt, fast prototyping versus full quality tiers, quad or triangle topology, PBR materials, HighPack 4K textures, T-pose or A-pose characters for rigging, or splitting a finished mesh into parts and retexturing it with Bang!. Keywords: Rodin Gen-2.5, Hyper3D, Deemos, image to 3D, text to 3D, part segmentation, texture delight.

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, extending, or editing video with Gemini Omni models on Scenario via MCP: text-to-video, image-to-video from a first frame, reference-to-video keeping a character, product, or place consistent, appending footage to a clip, restyling footage by prompt (season, wardrobe, art style), native audio with dialogue and ambience, or picking between first-frame and reference conditioning. Keywords: Gemini Omni Flash, 1.1 Flash, Google, T2V, I2V, V2V, extend, subject consistency.

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 editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1. Keywords: Luma Labs, Uni-1, Photon, txt2img, img2img, image edit, reference images, webSearch, reasoning image model.

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 a Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.

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 creating game art through Scenario MCP: sprites, sprite sheets, icons, props, loot, tilesets and autotile terrain transitions, seamless tiles, isometric buildings, top-down maps, pixel art, UI buttons and panels, parallax background layers or depth planes for side-scrollers, and character or concept art; or when assets need transparent backgrounds, background removal, style-consistent variation batches, upscaling, pixel-grid cleanup, or PNG export for Unity, Godot, or Unreal.

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。

DNA sequence profiling and genetic variant characterization

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。

Code-drawn stills, drawing timelapses, films, explainers and interactive web animations in 32 styles, with composed scores. Matches a reference style, keeps characters consistent, teaches drawing. Deterministic, no generated assets.

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。

Reviews or drafts the agreement that pays an employee to settle their claims — and flags the statutory conditions that decide whether it actually binds them. Works under s.203 ERA 1996 for England & Wales. Flags apparent s.203 condition gaps for a solicitor to confirm (writing, specific complaints, independent adviser, identification, insurance, statement of conditions), drafts the substantive terms (sum, tax characterisation, confidentiality, references, restrictive covenants, indemnities), and surfaces enforceability risks. Does not rule on validity and is not legal advice. Use when the user says 'review this settlement agreement', 'draft a settlement agreement', 'compromise agreement' (old term), or asks whether an agreement is binding.

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 the NIST AI Risk Management Framework (NIST AI 100-1 + the NIST AI 600-1 Generative AI Profile) to a specific AI system, governance question, or impact assessment. Three modes — consult, governance plan, full assessment — all cite Subcategories (`GOVERN 1.1`) and Profile Action IDs (`GV-1.2-001`) verbatim. Use when the user mentions the AI RMF, NIST RMF, NIST AI 100-1, NIST AI 600-1, GenAI Profile, the four functions (Govern / Map / Measure / Manage), the trustworthy AI characteristics, the 12 GAI risks (confabulation, harmful bias, information integrity, CBRN, data privacy, etc.), or asks "what does NIST say about X" for an AI system.

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Scrambled characters resolve into the real text on hover or view — a techy decode for a short label. The resolved text is the real, accessible content; under prefers-reduced-motion it shows instantly.

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。

Deconstruct a novel for game adaptation. Compress a raw novel, deconstruction library, or writing project into a SOURCE_BIBLE with cited textual evidence, extracting world rules, player verbs, spaces, character will, systems, and visual anchors — without inventing a genre yet. Use for gameable book analysis, analyze a novel for game adaptation, extract game systems from a book. 游戏化拆书。把原始小说、拆文库或写作工程压缩成有必要原文证据的 SOURCE_BIBLE，提取世界规则、玩家动词、空间、角色意志、系统和视觉锚点，不提前发明游戏类型。用于游戏化拆书、分析小说的游戏改编基础等需求。

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。

Count Korean text deterministically with exact grapheme, line, and byte contracts for self-intros and form limits.

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 multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.

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 deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.

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 patterns for Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.

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 blueprint for hierarchical finite state machines (HSM) and pushdown automata for complex AI/character behaviors. Covers state stacks, sub-states, transition validation, and state context passing. Use when basic FSMs are insufficient OR implementing layered AI. Keywords state machine, HSM, hierarchical, pushdown automata, state stack, FSM, AI behavior.

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 blueprint for fighting games including frame data (startup/active/recovery frames, advantage on hit/block), hitbox/hurtbox systems, input buffering (5-10 frames), motion input detection (QCF, DP), combo systems (damage scaling, cancel hierarchy), character states (idle/attacking/hitstun/blockstun), and rollback netcode. Based on FGC competitive design. Trigger keywords: fighting_game, frame_data, hitbox_hurtbox, input_buffer, motion_inputs, combo_system, rollback_netcode, cancel_system, advantage_frames.

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 blueprint for Action RPGs including real-time combat (hitbox/hurtbox, stat-based damage), character progression (RPG stats, leveling, skill trees), loot systems (procedural item generation, affixes, rarity tiers), equipment systems (gear slots, stat modifiers), and ability systems (cooldowns, mana cost, AOE). Based on expert ARPG design from Diablo, Path of Exile, Souls-like developers. Trigger keywords: action_rpg, loot_generator, rpg_stats, skill_tree, hitbox_combat, item_affixes, equipment_slots, ability_cooldown, stat_scaling.

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 patterns for branching dialogue systems including dialogue graphs (Resource-based), character portraits, player choices, conditional dialogue (flags/quests), typewriter effects, localization support, and voice acting integration. Use for narrative games, RPGs, or visual novels. Trigger keywords: DialogueLine, DialogueChoice, DialogueGraph, dialogue_manager, typewriter_effect, branching_dialogue, dialogue_flags, localization, voice_acting.

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 blueprint for romance games and dating sims (Tokimeki Memorial, Monster Prom, Persona social links) focusing on affection systems, multi-stat relationships, dated events, and route branching. Use when building relationship-centric games, social simulations, or otome games. Keywords romance, dating sim, affection system, relationship stats, date events, character routes, love interest.

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 patterns for RPG/action ability systems including cooldown strategies, combo systems, ability chaining, skill trees with prerequisites, upgrade paths, and resource management. Use when implementing unlockable abilities, character progression, or complex skill systems. Trigger keywords: PlayerAbility, AbilityManager, cooldown, SkillTree, SkillNode, prerequisites, can_use, execute, ComboSystem, ability_chain, global_cooldown, charge_system, upgrade_path.

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。

Bevy ECS skill for character stats. Keywords: Bevy, stats, attributes, modifiers, ECS.

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。