「traits」の検索結果

Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image generation.

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Master Rust async programming with Tokio, async traits, error handling, and concurrent patterns. Use when building async Rust applications, implementing concurrent systems, or debugging async code.

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Generate, visually expand, optimize, diagnose, and route structured AI image prompts for adult female portraits. Use for lifestyle, curve-focused, fashion, oriental, fantasy, realism, beauty, CCD, low-key cinematic, studio, sport, travel, and e-commerce portrait requests; onboarding or usage help; parameter recommendations; prompt rewrites; image-to-prompt preparation; direct image generation; or identity- and product-preserving edits from authorized reference images. Preserve explicit user parameters and locked reference subjects while creating a coherent photography-directed scene.

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Half-hour television comedy craft (半小时情景喜剧/笑点力学/困境机器/多机位单机位格式) — merged from Evan S. Smith's Writing Television Sitcoms (premise-driven comedy in three levels, nine predicaments, six character mixes, setup/punchline mechanics, toppers, running gags, ten comedic nuances, format rules), Neil Landau's sitcom seven-step formula and comedy-room models, Richard Blum's act-length and set-count parameters, Phoebe Waller-Bridge's Fleabag scripts (direct address as a season-long device, six-episode skeleton), Pamela Douglas on dramedy, Kam Miller on complementary comic traits, and Jesse Armstrong on jokes carrying tragedy. Use when writing or fixing a sitcom episode or spec, raising joke density per page, designing a comic predicament that generates stories, choosing between multi-camera and single-camera format and page counts, structuring cold open/acts/tag, writing a half-hour pilot, running a comedy punch-up, or making a drama funny without shifting genre.

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Generate one or more standalone Meta image-ad creatives via Nano Banana 2 / Nano Banana Pro (Gemini Flash Image family) through the Arcads external API. Locks the model family, auto-strips platform chrome, enforces edge-safe layouts. Use when the user asks for a "Nano Banana ad", "Gemini image ad", "nano-banana-2 ad creative", "make a static image ad with Gemini", or anchors on a need for photoreal / lifestyle / multi-reference / handheld-object / clay-texture ad creatives (sticky-note flatlays, held-whiteboard signs, lifestyle portraits, ingredient collages, OOH photography). Does NOT trigger on ChatGPT Image cues — use chatgpt-image-ad for those.

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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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Build Manhattan, Miami, QQ, and locuszoom-style regional plots from GWAS, TWAS, PWAS, and QTL summary statistics with correct genomic-inflation diagnostics, multi-trait overlays, lead-SNP labeling, and LD-aware regional rendering. Use when visualizing association results across the genome, comparing two traits, computing genomic inflation lambda, or zooming into a locus with LD coloring.

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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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Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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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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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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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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Core library: traits, types, logging, math, fiber, allocators, and STL containers.

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

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。