'traits' 검색 결과

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

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Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with eQTL/sQTL/pQTL/mQTL, distinguishing shared causal variants from LD-driven coincidence, handling allelic heterogeneity, choosing between single-causal vs multi-causal methods, picking PP.H4 thresholds, running sensitivity over p12, or harmonising summary statistics for colocalization.

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Writes and migrates Swift Testing framework tests with @Test, @Suite, #expect, #require, confirmation, traits, withKnownIssue, Attachment.record, processExitsWith exit tests and capture lists, Test.cancel, Issue.record warnings/manual failures, XCTest-to-Swift Testing migration, Xcode 27 interoperability modes, XCUITest UI-test boundaries, performance/snapshot boundaries, mocking, async patterns, and test organization. Use when writing tests, converting XCTest assertions such as XCTUnwrap or XCTFail, reviewing advanced Swift Testing API availability, or deciding when to keep XCTest/XCUITest.

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Build and audit SwiftUI, UIKit, and AppKit accessibility for VoiceOver, Voice Control, Switch Control, Full Keyboard Access, Dynamic Type, focus restoration, labels/traits/actions, traversal, custom rotors, NSAccessibility, XCTest checks, adaptive system preferences, and App Store accessibility declarations. Use when implementing accessible UI, fixing an accessibility audit, testing assistive-technology behavior, or substantiating App Store Accessibility Nutrition Labels.

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LuisaCompute API guide covering the DSL (kernels, callables, structs, buffers, atomics, control flow, dispatch), the runtime API (Context, Device, Stream, buffers, images, ray tracing, rasterization), AST usage markers and AST-to-XIR lowering, the core library (traits, types, logging, math, fiber, allocators, STL containers), and vstd containers (HashMap, queues, pools, variant, smart pointers, utilities).

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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.

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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.

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Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().

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Transform ordinary snapshots—including daily moments, portraits, objects, pets, food, interiors, streets, architecture, landscapes, travel, and humanistic scenes—into the restrained Shijing Paper Zine visual style. Use when the user wants any real photo turned into a spacious paper-editorial composition with an irregular photographic fragment, simplified source-derived drawing, one structural print color, tactile torn-paper texture, minimal micro-copy, a consistent multi-image series, or a source-to-poster comparison.

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Use when the user wants a marketing image GENERATED or an image prompt written — personal brand portraits, logos and monograms, campaign key visuals, day-to-day social graphics, editorial art, infographics, web hero mockups, and quote graphics. Reads brand identity from project context, composes an on-brand prompt, generates via gpt-image-2 when an API key exists, otherwise outputs paste-ready prompts for DALL-E 3, MidJourney, Leonardo, or Ideogram. Trigger on 'design an image', 'make me a logo', 'MidJourney prompt', 'key visual for the campaign', 'generate a graphic for this post', 'I need a visual and have no designer'. Not for — briefing a human designer on a static, see `42-image-brief-global`; a slide sequence, see `43-carousel-brief-global`; the brand rules themselves, see `46-brand-guideline-global`; scoring a finished design, see `47-design-review-global`.

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Use when the user needs to define, document, or enforce how the brand SOUNDS in writing — three personality traits, a five-dimension tone table, vocabulary to use, avoid, and ban, sentence rules, before and after rewrites, platform adaptation, and a copy approval checklist. Every content skill reads it before writing. Trigger on 'brand voice', 'tone of voice', 'voice guidelines', 'banned words list', 'this copy does not sound like us', 'every writer sounds different'. Also use when the user cannot say why a draft feels wrong. Not for — the visual side of brand, see `46-brand-guideline-global`; positioning and the message, see `58-positioning-global`; approving one specific piece, see `62-marketing-review-global`.

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Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.

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Image prompting skill for Nano Banana (NBP/NB2) and GPT Image 2.5 (Flare/Sunburst). Writes ready-to-use prompts with model/quality/size recommendations. Use when: "нарисуй", "сгенерируй картинку", "image prompt", "промпт для картинки", blog covers, slides, posters, product shots, UI mockups, storyboards, character sheets, edit/colorize, style transfer, vision analysis, image-to-prompt, nb, NBP, NB2, gpt-image-2.5, multi-panel grids, ecommerce product photography, fashion editorial, food/beverage ads, cinematic portraits. Do NOT use for: video (use video skill), 3D models, audio, non-image tasks.

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Classifies existing tests by standard traits and reports their distribution. USE FOR: tagging all tests with category attributes, categorizing/tagging/ labeling each test, compare happy vs error paths, audit the test mix, describe coverage shape by test type, or tag then verify the project builds. Read bodies when names mislead. Apply canonical attributes; otherwise report only. DO NOT USE FOR: requests owned by test-anti-patterns, coverage-analysis, crap-score, test-gap-analysis, code-testing, or migration skills.

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Convert .NET tests from xUnit.net v2/v3 to MSTest v4 while preserving VSTest or MTP. Use for replacing xunit packages, Fact/Theory/InlineData/MemberData, assertions, IClassFixture/ICollectionFixture, ITestOutputHelper, TestContext cancellation, traits/Owner, skips, timeouts, and xUnit parallelization. Also use when a "convert xUnit to MSTest" request may already be migrated: inspect and report the no-op. Do not use for xUnit v2-to-v3, MSTest upgrades, NUnit/TUnit conversion, or runner-only VSTest-to-MTP migration.

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NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published GWAS. Query studies, associations, variants, traits, genes, summary stats. Build PRS candidates, analyze pleiotropy, fetch stats for Manhattan plots. No auth.

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Analyzes a company's content to extract and codify their brand voice into a comprehensive style guide. Reads website copy, blog posts, emails, and social media to identify tone, vocabulary patterns, sentence structure, personality traits, and word preferences. Generates a brand-voice-guide.md and reviews new content against it.

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API reference: Swift Testing framework. @Test, @Suite, #expect, #require, traits, parameterized tests, migrating from XCTest.

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Audits Rust code for unsafe blocks, ownership issues, and Cargo dependency risks. Use when reviewing Rust code or before merging Rust changes.

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Use when switching AI persona/language style. Syncs 13+ character profiles (Ding Yuanying, Lin Daiyu, Lu Xun, Li Yunlong, etc.) from GitHub, generates persona prompts with thinking patterns, rhetoric, and speech traits.

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Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.

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Think and work like an expert Botanist. Use when a task calls for Botanist judgment. Reasons from morphology, floras (FNA/Jepson/eFlora), voucher herbarium specimens (Darwin Core, Index Herbariorum), nomenclature (IPNI/POWO/Madrid Code), APG IV phylogeny, DNA barcoding (rbcL/matK/ITS2), community ecology (vegan adonis2/betadisper, TRY traits), and CITES/ABS ethics; treats vegetative mis-ID, pseudoreplication, PERMDISP confounds, and unvouchered GBIF records as first-class failure modes.

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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.

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Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with eQTL/sQTL/pQTL/mQTL, distinguishing shared causal variants from LD-driven coincidence, handling allelic heterogeneity, choosing between single-causal vs multi-causal methods, picking PP.H4 thresholds, running sensitivity over p12, or harmonising summary statistics for colocalization.

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