「effects」の検索結果

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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Replaces the voice in a video or audio file with one from the Novoads voice catalog, over the REST API. Converts the SPEECH in the source to a voice you pick, keeps the original timing so lip-sync survives, and returns audio that gets muxed back over the untouched picture locally. Casts by MEASURING the source voice against auditioned candidates instead of reading labels, refuses a source with no speech before anything is charged, fences sound effects and music so they survive from the original track, and verifies the finished file by transcribing it. Use when an actor's voice sounds robotic, flat, thin or simply wrong, and for "change the voice", "voice swap", "swap the voice on this ad", "re-voice my ad", "the actor's voice sounds bad", "make him sound human", "different voice for this video", "speech to speech", "same video better voice". Not for narrating a silent clip (that is a voice-over), not translation, and it changes neither pronunciation nor accent.

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。

Implement, migrate, review, and diagnose iOS Liquid Glass interfaces in SwiftUI and UIKit. Use for system chrome, custom glass effects, transitions, accessibility, and version-specific issues in native iOS apps.

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pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.

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Generate a soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that only appear after sustained play. Primarily used in Polish and Release phases.

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Unity 6 audio system guide. Use when working with sound effects, music, AudioSource, AudioClip, AudioListener, Audio Mixer, spatial audio, or audio optimization. Covers 3D sound, mixer groups, snapshots, and audio import settings. Based on Unity 6.3 LTS documentation.

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Gameplay Ability System patterns for abilities, effects, attributes, and tags including recursion safety and lifecycle management.

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, implementing, or reviewing frontend interaction motion, page transitions, scroll animation, Framer Motion, GSAP, Lottie, CSS animation, motion intensity, animation performance, reduced-motion behavior, or cinematic but accessible UI motion. Do not use for SVG-only path drawing, Canvas/WebGL rendering, or ordinary hover states; Chinese triggers include motion design, interactive effects, page transitions, scroll animation, Framer Motion, GSAP.

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。

Implement event-driven side effects that react to domain events

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Use when a task needs the judgment of a Blockchain Engineer — applying the checks-effects-interactions pattern to prevent a reentrancy exploit, auditing a privileged/administrative function for missing access control, designing a manipulation-resistant oracle for on-chain price data, minimizing gas cost through storage-write design, or deciding whether a smart contract needs an upgradability pattern before deployment.

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 comprehensive histone mark maps by aggregating narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is this histone mark present in my tissue?" by combining peak calls from multiple studies into a union peak set with confidence annotations. Handles cross-lab batch effects, broad vs narrow marks, and ENCODE blocklist filtering.

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Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.

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Analyzing behavioral experiment data — RT/accuracy preprocessing, mixed-effects models, and robust inference.

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Assess business impact of a proposed change. Use when the user says "impact assessment", "impact analysis", "what will this change affect", "change impact", "who is impacted", "ripple effects", "downstream impact", "impact mapping", "what breaks if we change this", "readiness for change" - even if they don't explicitly say "impact assessment".

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Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.

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Judges whether a user's input suits the Vibe Creating style of video-prompt writing, and when it does, distills single-scene prompts, multi-shot descriptions, emotional imagery, or mixed input into prompts that are easier for a video model to generate from — while preserving any user-specified dialogue, voiceover, music, sound effects, and other hard constraints. Use when a user wants to turn an idea, story, feeling, or rough/over-specified prompt into a strong text-to-video prompt (Seedance, Sora, Kling, Veo, Runway, etc.), or asks to "rewrite", "improve", "clean up", or "vibe-ify" a video prompt. Do NOT use for long narrative films that need precise word-for-word dialogue sync, industrial shot lists meant to be executed verbatim, or functional/UI demos and step-by-step tutorials.

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。

Pre-landing PR review. Analyzes diff against main for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues.

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。

ออกแบบ animation, motion brief, AfterEffects spec, Lottie export สำหรับ UI, logo reveal, explainer video

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Use when adding animations with Motion Vue (motion-v) - provides motion component API, gesture animations, scroll-linked effects, layout transitions, and composables for Vue 3/Nuxt

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 the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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 build cohesive web pages with custom visual identity — landing pages and portfolios get rich anatomy + section archetypes; any other page type (blog, about, pricing, contact, coming-soon, dashboard, admin, e-commerce, custom) is supported through generic anatomy + skeleton. Use this skill whenever the user mentions: landing page, portfolio, personal site, hero section, marketing page, product page, sales page, splash page, blog page, about page, pricing page, contact page, coming-soon, waitlist, dashboard, admin panel, e-commerce store, redesign my site, design my portfolio, build a portfolio, work showcase, hire-me page, perfect-ui, perfect landing, or asks to create any web page surface. Two tiers: special (landing | portfolio — rich treatment) and generic (everything else — universal craft toolkit). Self-contained 8-phase pipeline: vibe discovery → visual direction (color/typography/mood) → custom icon set (NO emoji, NO icon library) → AI-generated visuals → optional shader effects layer → inline plan + implement on Next.js + Tailwind + shadcn. Always proposes effect layer as optional. No refusals; outputs distinctive sites, not AI slop.

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Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.

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This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.

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。

Audit code against NASA JPL's Power of Ten rules, extended for AI-assisted development. Use when checking code for dangerous complexity, unsafe control flow, resource leaks, hidden side effects, missing validation, weak CI gates, AI-generated artifacts, or missing failure-mode tests.

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。