「localization」の検索結果

Translate books, documents, AiNiee projects, Unity localization caches, or flat MTool JSON dictionaries end to end on the coding agent itself without the AiNiee app. Use for requests such as "用 agent 翻译这本书", "把 epub 翻译了", "翻译 Unity CSV/cache", "把这个 MTool JSON 翻译了", or "AiNiee-style translation" that need a locked glossary, resumable batches or bounded subagent groups, terminology consistency, source-aware QA, validated cache writeback, and export. For MTool, preserve the left JSON key as immutable source text and write only the right value as translation.

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。

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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。

Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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。

Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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。

Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.

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。

GodoMaster — Godot 4.x game development intelligence: GDScript, nodes/scenes, 2D/3D rendering, physics, animation, UI, audio, input, shaders, networking, performance, export, file I/O, testing, localization, AI behavior, asset pipeline, and architecture tooling. Complete workflow for indie and professional game development based on official Godot documentation.

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。

Bulk-localize subscription and in-app purchase display names across all App Store locales using asc. Use when you want to fill in subscription/IAP names for every language without clicking through App Store Connect manually.

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。

Sync, validate, and apply App Store metadata with the current asc canonical metadata workflow. Use when updating metadata, localizations, keywords, or migrating legacy fastlane metadata.

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。

Product marketing and go-to-market strategy for B2B SaaS launches and positioning. Use this skill when planning a product launch, developing positioning and messaging, analyzing competitive landscape, building customer advocacy programs, designing a win/loss interview program, working out why we lose deals, designing GTM strategy, creating category positioning, planning analyst briefings, or deciding which country to expand into and how far to localize. Also use it for brand marketing and demand creation — how much to spend on brand versus performance, what to measure when a brand campaign produces no conversion event, and how to answer "we cannot attribute it". Also triggers on: positioning, product launch, competitive intelligence, messaging, customer advocacy, win/loss analysis, win/loss interviews, why we lose deals, closed-lost analysis, loss reason, close-reason field, competitive displacement, displace an incumbent, rip and replace, competitive takeout, switch from a competitor, win deals from a competitor, incumbent contract, renewal window, auto-renewal notice period, switching costs, EU Data Act, cloud switching rights, why do we lose to no decision, GTM, category design, analyst briefing, G2 review, international expansion, market entry, expand into Europe, which country should we launch in next, localization, translate our site, in-country go-to-market, regional GTM, is cold email legal in Germany, brand marketing, brand awareness, brand vs performance, brand versus demand gen, demand creation, demand capture, 95-5 rule, out of market buyers, category entry points, distinctive brand assets, mental availability, brand tracking, brand health, brand lift, share of search, how do we measure brand, justify brand spend, brand budget.

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。

Builds React Native and Expo apps in TypeScript — navigation with React Navigation or Expo Router, lists, forms, styling, gestures and animations with Reanimated, safe areas, accessibility and localization; networking with TanStack Query, response validation, persistence with MMKV, AsyncStorage, SQLite and secure storage; permissions, push notifications, deep links, background tasks, native modules and config plugins; performance with Hermes and the React profiler; and release with EAS Build, EAS Submit and EAS Update or bare Gradle and Xcode builds. Use when writing or changing React Native code, adding a native capability, configuring Expo, preparing a store build or an over-the-air update, or when the user asks how to do something in React Native, even if they do not name the library.

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。

Builds Flutter apps with guides for everything specific to Flutter and Dart — layouts, responsive UI, forms, animations, theming, declarative routing with go_router, widget previews and home screen widgets; HTTP, JSON serialization, local databases and caching; plugins, platform channels and native views; isolates and app size; accessibility and localization; Effective Dart, Dart 3 features and pattern matching; and macOS setup. Use when writing or changing Flutter UI, data or native integration code, or when the user asks how to do something in Flutter, even if they do not name the topic.

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。

Bu template'in tasarim sistemi referansi — renk (colorScheme + AppThemeColors semantic token'lari), tipografi (textTheme slotlari, Poppins/Inter), spacing/radius/golge/sure token'lari (AppPaddings, AppRadius, AppShadows, AppDurations, AppIconSizes), paylasilan widget katalogu (AppPrimaryButton, dort-durum widget'lari, AppTextField, sheet'ler), 8 varyantli light/dark tema ve easy_localization. Kullanici UI yazarken/duzenlerken "renkleri projeye gore kullan", "bu butonu projeye gore yaz", "hangi spacing/radius", "tasarim sistemine uydur", "style guide", "tema token'lari" dediginde kullan.

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。

Write, configure, and deploy Netlify Edge Functions (Deno runtime at the network edge) in TypeScript/JavaScript. Use when adding request/response manipulation at the edge — auth middleware, geolocation redirects, A/B testing and personalization, content localization, redirects/rewrites, SSR at the edge, or transforming responses — or when configuring path routing, response caching, or edge error handling. Triggers on tasks like "add auth middleware", "geo-based redirect", "A/B testing at the edge", "rewrite requests", or editing files in netlify/edge-functions.

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。

Sync, validate, and apply App Store metadata with the current asc canonical metadata workflow. Use when updating metadata, localizations, keywords, or migrating legacy fastlane metadata.

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。

Bulk-localize subscription, subscription-group, and in-app purchase display names across App Store locales using asc, including API 4.4.1 version-scoped v2 resources. Use when filling or updating subscription/IAP names and descriptions without App Store Connect UI 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。

A skill that localizes contents into given locales.

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。

End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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。

Detect i18n/locale keys that are removed from source but remain in locale files, or keys added to locale files without source usage.

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。

Discover and reuse owner resource structure, then validate paths, RSI, audio, maps, attribution, and localization companions.

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。

Place contracts and behavior across authority, prediction, presentation, localization, and assembly boundaries.

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。

Port one complete feature family with provenance, current-API adaptation, repository ownership, localization, resources, and verification.

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 complete SS14 features across ownership, ECS, networking, resources, localization, UI, and 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。

Review correctness, access, repository ownership, authority, localization, compatibility, resources, scope, and verification evidence.

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。