650 件 ・ 関連度順
概要と使いどころ
Set up a Godot 4.7 3D scene: Node3D transforms, Camera3D, lighting (DirectionalLight3D/OmniLight3D), WorldEnvironment for sky/ambient/tonemap/post, MeshInstance3D materials, and GridMap for tile-based 3D levels. Use when building a 3D scene in a Godot project, placing cameras/lights, configuring environment and post-processing, or working with Node3D/.tscn 3D content and GridMap.
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。
Declare bankruptcy on a long-lived AI agent's accumulated memory — audit what it currently believes, separate ground truth from stale and wrong, purge deliberately, restate the truths that survive, and log what was lost. Use when an agent keeps acting on outdated facts, contradicts itself across sessions, 'remembers' things wrong, or after a reorg/pivot makes its worldview obsolete. Produces a belief audit, a keep/correct/purge ledger, a restated ground-truth file, and the bankruptcy record.
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。
Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.
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。
Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.
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。
Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.
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 starting or navigating any American Anthropologist (AA) manuscript as the entry point. Routes to the right AA sub-skill based on lifecycle stage, subfield (sociocultural, archaeology, biological, linguistic), and section type (Research Article, Vital Topics Forum, World/Public/Multimodal Anthropologies, Review Essay). It dispatches; it does not draft content.
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。
Search and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more
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。
Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.
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。
Assemble a narrated-UGC "stitch reply" ad from a config — a single spoken VO carries a verbatim testimonial while ~30 per-cut i2v clips (one creator across ~5 wardrobes in ~3 worlds, plus product B-roll) are each trimmed to their EDL window built from the VO's Whisper word boundaries and hard-concatenated via filter_complex concat (never the demuxer, which drops audio on a duration mismatch), the VO mixed over an optional sidechain-ducked instrumental bed (−20dB, 20 to 1) so the VO stays on top, karaoke-pop captions burned on every word throughout (VEED Whisper preset, re-spelled against the locked script), a landing-page scroll rendered as FFmpeg zoompan over a Playwright PNG (not i2v), and closed on the brand's real end-card PNG — never AI-rendered text. This is the FREE deterministic assembly stage (trim-to-EDL + filter_complex concat + VO and music mix + karaoke captions + landing-page zoompan + end-card append), run with the shared stitch-videos-ffmpeg montage.py helper that installs with this package; the VO, creator, start-frames, and clips come from create-vo-elevenlabs / create-image-gpt-image-fal / create-image-fal / create-video-fal. Use for the narrated-ugc-wardrobe-stitch format.
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。
Assemble a cosmic-mythology-voiceover reel from a config — one spoken voiceover carries the whole narrative while N curated stills in one chosen look are weighted beat-synced across the delivered VO duration (cut_dur = VO_dur times weight over the weight sum, so emotional beats hold longer), Ken-Burns-zoomed per still (scale 2x, center crop, zoompan, fade-in first and fade-out last), ffmpeg-concatenated, the VO composited under the picture (libx264 crf18 plus aac), the ONE on-screen hook line faded on over the open with a drawtext alpha window, and Whisper/VEED captions burned along the bottom — never in-world text on a still. This is the FREE deterministic assembly stage (weighted sequence plus Ken-Burns plus concat plus VO composite plus hook overlay plus caption burn); the VO and the stills come from create-vo-elevenlabs and create-image-fal. Use for the cosmic-mythology-voiceover format.
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 accepting ANY real-world payment — Apple Pay, Wallet passes, Tap to Pay, Orders in Wallet. NOT in-app purchase / digital content (use axiom-integration). Covers entitlements, certs, signing, App Review payment rules.
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。
Integrate Apple Pay payments and Wallet passes using PassKit. Use when adding Apple Pay buttons, creating payment requests, handling payment authorization, adding passes to Wallet, configuring merchant capabilities, managing shipping/contact fields, or working with PKPaymentRequest, PKPaymentAuthorizationController, PKPaymentButton, AddPassToWalletButton, PKPass, PKAddPassesViewController, PKPassLibrary, Wallet pass distribution, or Apple Pay checkout flows for physical goods, real-world services, donations, and eligible recurring payments.
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 RoadRunner Scenario programmatically from an OpenSCENARIO 1.x (.xosc) file using the `roadrunner-scenario-authoring` skill. Use when the user wants to recreate a scenario from a .xosc file, interpret an OpenSCENARIO file and build it programmatically, reconstruct a .xosc as a RoadRunner scenario, generate a MATLAB script from a .xosc file, or convert an OpenSCENARIO file to MATLAB code. Do NOT use when the user says "import" a .xosc file — that means they want RoadRunner's built-in importScenario API, not programmatic reconstruction. Handles position translation (LanePosition and RoadPosition to world coordinates), construct mapping, relative references, trajectory/route handling, parameter expressions, catalog references, and phase logic topology.
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 building or debugging UE5 gameplay: a character with double jump or dash, abilities with cooldowns, enemy AI that patrols, chases and attacks, a health bar or HUD; choosing Blueprint vs C++, GAS or not, StateTree vs Behavior Tree, EQS or Mass; setting up Enhanced Input, Common UI or UMG; replication, RepNotify, save games, World Partition state; "Blueprints are slow", Tick, casts and hard references; "ability won't activate", "enemy ignores the player"; or writing automated gameplay tests with injected input.
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 lighting or rendering a Godot 4.7 3D scene: pick a renderer (Forward+, Mobile, Compatibility), choose GI (SDFGI, VoxelGI, LightmapGI, reflection probes), bake lightmaps without a mouse, tune WorldEnvironment, tonemapper (AgX, ACES, Filmic), glow, fog, volumetric fog and god rays, shadows and shadow acne, quality presets for PC and phones, Compositor post effects, or when 'my scene looks flat', 'too dark indoors', 'light leaks', 'lightmap is black', 'looks different on mobile'.
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 story must unfold across a sequence of Scenario images: a comic page or strip, manga or webtoon panels, a children's storybook, a film, game, or ad storyboard, animatic pre-viz frames, or any shot list where the same characters and world must persist from panel to panel and dialogue must stay legible. Keywords: storyboard, comic, panel, sequential art, storybook, shot list, pre-viz, animatic, scene to scene, character 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 video with Seedance models on Scenario via MCP: text-to-video, image-to-video, first and last frame anchors, reference-to-video from identity, product, or world references, prompt-based editing, extending a clip, audio-conditioned motion or voice, shot sound without music, first-frame versus reference conditioning, or an output blocked by moderation. Keywords: Seedance 2.5 and 2.0, ByteDance, T2V, I2V, V2V, native audio, OutputVideoSensitiveContentDetected.
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。
A minimal test skill that greets the user and demonstrates the ASM publish workflow.
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。
World-class prompt powerhouse that generates production-ready mega-prompts for any role, industry, and task through intelligent 7-question flow, 69 comprehensive presets across 15 professional domains (technical, business, creative, legal, finance, HR, design, customer, executive, manufacturing, R&D, regulatory, specialized-technical, research, creative-media), multiple output formats (XML/Claude/ChatGPT/Gemini), quality validation gates, and contextual best practices from OpenAI/Anthropic/Google. Supports both core and advanced modes with testing scenarios and prompt variations.
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。
Audits the transition from legal AI output to reliance, recommendation, execution, or real-world commitment. Use it before an AI-generated analysis, memo, contract review, compliance finding, or workflow output is acted upon to check whether authority, evidence, mandate, uncertainty, procedure, and review conditions remain sufficient.
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。
Turn a novel into a fully playable game on the selected target platform. Orchestrates the whole adaptation pipeline — requirements intake, gameable deconstruction, concept selection, world and visual design, target-runtime build, and evidence-based QA — for a novel in any language. Use for novel to game, story to game, book to game, adapt this novel into a game, turn this book into a playable prototype, make an interactive story or text adventure from this novel. NovelToGame 总入口。把任意语言的原始小说、拆文库或 oh-story 写作工程转成有原著依据、可在目标平台完整游玩的游戏，编排游戏化拆解、概念选择、游戏与视觉设计、目标运行环境构建和证据化质量验证。用于小说转游戏、把这本书做成游戏、把小说改成互动小说 / 互动叙事 / 文字冒险游戏等需求。
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。
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。
Anchor a multidisciplinary unit in a real-world phenomenon that requires multiple subject lenses to understand. Use when designing integrated or phenomenon-based units across disciplines.
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。