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概要と使いどころ
Design a Seedance 2.0 science, education, or general-knowledge explainer using layered handmade papercraft, miniature diorama depth, physical paper mechanisms, clear knowledge beats, restrained miniature camera, tactile sound, and stable reference roles. Use for one 4–15 second clip or a longer multi-clip plan; not for flat vector cartoons, live action, or paper-texture-only styling.
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 a Seedance 2.0 prompt for a rough hand-drawn luminous entity that physically contacts a live-action space, continuously transforms while remaining traceable, escapes through adjacent geography, and is followed by a slightly delayed handheld camera. Use for playful 4–15 second mixed-media clips; not for horror, polished CG, unrelated cuts, or simple visual filters.
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 original relaxed black-pen graphics from concepts or visual references, including sparse illustrations, narrative scenes, animals, objects, abstract relationships, icons, and logo marks. Use when the user wants quick hand-drawn line energy, causal story staging, non-equilibrium proportion, shorthand forms, open contours, structural line drift, misregistered junctions, selective retracing, physical pen texture, or a transferable illustration system that must not collapse into clean vector minimalism or polished period-sketch drawing.
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。
Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language 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。
Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.
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 channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.
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。
Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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 channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.
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, derive, optimize, explain, and diagnose cinematic Eastern xianxia camera direction, storyboards, motion choreography, pacing, continuity locks, keyframes, and per-shot image-to-video prompts. Use for 仙侠运镜、镜头语言、分镜、推拉摇移、跟拍、环绕、航拍、打斗追逐、情绪镜头、首尾帧、视频提示词、参考视频诊断, when users ask “怎么运镜/不会运镜/给案例/帮我改运镜”, or when an input contains invalid, contradictory, physically implausible, non-camera, or off-topic instructions that need actionable feedback. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 30 seconds, 16:9, six shots, narrative-adaptive pacing, epic scale, and selective high saturation when unspecified.
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 complete video ad scripts for 15s, 30s, and 60s formats with shot-by-shot breakdowns, voiceover scripts, on-screen text, B-roll suggestions, music direction, storyboard outlines, and three proven video frameworks
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。
Legacy v1 (voiceover-led, GSAP + HyperFrames). Use when making, directing or rescuing a product launch video, promo, teaser, demo reel, sizzle, feature-announcement film or animated product explainer built from code — and when an existing one looks basic, reads as a slideshow, has reveals that miss the voiceover, an invisible cursor, an inaudible sound effect, a clipped or too-quiet mix, or every video coming out looking the same. Covers SaaS, apps, hardware, e-commerce, dev tools, services. Keywords include motion design, storyboard, voiceover sync, kinetic typography, GSAP, HyperFrames, Remotion, film grain, LUFS, ffmpeg.
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。
Fix "Fastly error: unknown domain" 500 errors when custom domains return "Domain Not Found" despite being created with domain-v1 CLI. Use when: (1) fastly domain-v1 create succeeds but domain returns 500, (2) Domain shows activated:false in API response, (3) Classic domain API returns "deprecated" error, (4) Custom domain works on edgecompute.app but not on your domain. Covers Fastly Compute domain setup post-September 2025.
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 asked to make a launch video, product film, motion design piece, UI motion reel, Dribbble-style UI animation or animated brand/logo sting — anything rendered to MP4 from HTML with a cursor, morphing UI, springs, beat-synced music and SFX. Carries Chan's full creative direction (one shape never cut, blur content swaps, springs, real data, logo slams, no creative limits) and the proven build/render pipeline (pure seek(t), closed-form springs, Playwright subframes, ffmpeg tmix, numpy beat grid).
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 consistent characters, environments and props in Guaardvark's Cast Library and train LoRAs for them locally (reference photos → vision bible → sample plan → approved samples → training). Use when the user wants the same face or object across images, videos, a music video, or a Film Crew production, or asks to "train a LoRA".
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。
Connect this agent to a running Guaardvark (self-hosted AI studio) and check what it can do right now. Use first when the user mentions Guaardvark, asks to generate images/video/music/voice locally, run a Film Crew, train a LoRA, launch a swarm, or when a guaardvark MCP tool is missing or a call fails with "not reachable" / "plugin offline".
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 ONE topic, talking-head video, or photo into a finished Vox-style paper-collage explainer / ad video on the MuAPI platform (api.muapi.ai) + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Three input modalities: a topic (B-roll), a talking-head video (A-roll mode), or a single photo of a person/product anchored into the collage (C-roll mode).
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。
Starter template for a functional agent skill.
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 reference-image illumination and build accountable Blender lighting from shadow direction, shadow length, softness, exposed faces, environment fill, bounce, practical sources, and material response. Use for reference matching, daylight, interiors, exterior environments, product scenes, atmospheric lighting, or any task where lights must be physically and visually justified instead of added as arbitrary fill or rim lights.
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。
Plan, build, place, and validate physically meaningful Blender object assemblies, including manufactured part boundaries, mechanical seams, mounting interfaces, pivots, hierarchy, constraints, instancing, modular grids, connector planes, and precision snapping. Use when several objects must form one product, machine, architectural module, vehicle, furniture item, mechanism, or environment structure without incorrectly welding independent parts, eyeballing alignment, or leaving continuous shells as overlapping primitives.
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。
Orchestrate production-grade Blender work by probing the installed Blender version, retrieving relevant official Blender documentation, assigning visual design intent, classifying shape grammar and geometry/deformation/procedural/physics/shading needs, comparing direct Blender components with node systems, creating staged artifacts, and routing to specialist skills. Use for every non-trivial Blender creation, reconstruction, simulation, animation, environment, or scene-modification request, especially when the user does not know whether to use topology, Boolean, Array, curves, snapping, Displace, simulation, Geometry Nodes, or shading.
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, configure, test, bake, and validate Blender physical effects including Cloth, Soft Body, Rigid Body, collisions, constraints, Fluid, smoke, fire, Dynamic Paint, Ocean, Wave, particles, Boids, force fields, fracture, debris, and secondary effects. Use when gravity, inertia, collision, flexibility, pressure, flow, flocking, emission, or breaking causes the requested result.
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。
Forensic case investigation with evidence-graded findings, calibrated to the input. Use when the user asks to investigate a bug, trace what caused an incident, walk through unfamiliar code, or build a mental model of a code area before working on it.
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。
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
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。
Push interfaces past conventional limits with technically ambitious implementations. Whether that's a shader, a 60fps virtual table, spring physics on a dialog, or scroll-driven reveals — make users ask "how did they do that?"
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。