「aria」の検索結果

N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.

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Orientação de liderança técnica para equipes de engenharia, decisões de arquitetura e estratégia de tecnologia. Inclui analisador de débito técnico, calculadora de escalabilidade de equipes, frameworks de métricas de engenharia, ferramentas de avaliação de tecnologia e templates de ADR. Use ao avaliar débito técnico, escalar equipes de engenharia, avaliar tecnologias, tomar decisões de arquitetura, estabelecer métricas de engenharia ou quando o usuário menciona CTO, débito técnico, débito técnico, escalabilidade de equipes, decisões de arquitetura, avaliação de tecnologia, métricas de engenharia, métricas DORA ou estratégia de tecnologia.

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予算実績差異分析スキル。勘定科目タイプ（収益/費用）に応じた有利・不利差異の自動判定、 差異の分解（価格差異・数量差異）、重要度ランキング、根本原因の仮説提示を行う。 CSVデータのアップロードによる自動分析に対応。 Use when: 予算と実績の比較分析を行いたいとき。月次・四半期の予実管理レポート作成、 差異の原因分析、経営会議向けの予実サマリ作成に使用。 Triggers: "予実差異", "予算実績", "budget variance", "budget vs actual", "予算対比", "差異分析", "variance analysis"

Build or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

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Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.

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Retrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP. Used when a scientific agent must branch deterministically on resolved, ambiguous, not-found, invalid, unsupported, or unavailable variant outcomes; chain a resolved public variant into related literature or publication details; or preserve evidence provenance without accepting patient, phenotype, family, segregation, or private case data.

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Decompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership.

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Etsy product detail scraper: given an Etsy listing URL, returns full product detail including listingId, title, priceCurrent, priceOriginal, currency, images (all), description, shopName, shopUrl, rating, reviewCount, favorites, inCartCount, variations (with per-option price ranges), highlights, listedDate, relatedTags. Use when user mentions Etsy product, Etsy listing detail, Etsy item info, Etsy product page, scrape Etsy listing, extract Etsy product data, Etsy price and variations, Etsy product images, Etsy product description, Etsy shop from listing, Etsy favorites count, Etsy sale count, Etsy variations extraction, Etsy listing metadata, single Etsy product scrape, bulk enrich Etsy listing URLs, Etsy product details export. Also applies to competitor product monitoring, price and variation tracking on a specific listing, favorites/wishlist popularity tracking, description mining for SEO analysis, and any per-listing enrichment task.

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Amazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg. Use when user mentions Amazon product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.

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Score, annotate, and analyze the functional impact of genetic variants using AlphaGenome Variant Impact (AVI) scores. Query variants in chr:pos:ref>alt format, annotate VCF/tabular callsets, perform saturation mutagenesis window scans (1-based closed chr:start-end), and extract GENCODE v46 GTF gene/exon/junction coordinates all via the AlphaGenome Atlas API.

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Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

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Production-tested setup for Tailwind CSS v4 with shadcn/ui, Vite, and React. Use when: initializing React projects with Tailwind v4, setting up shadcn/ui, implementing dark mode, debugging CSS variable issues, fixing theme switching, migrating from Tailwind v3, or encountering color/theming problems. Covers: @theme inline pattern, CSS variable architecture, dark mode with ThemeProvider, component composition, vite.config setup, common v4 gotchas, and production-tested patterns. Keywords: Tailwind v4, shadcn/ui, @tailwindcss/vite, @theme inline, dark mode, CSS variables, hsl() wrapper, components.json, React theming, theme switching, colors not working, variables broken, theme not applying, @plugin directive, typography plugin, forms plugin, prose class, @tailwindcss/typography, @tailwindcss/forms

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Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.

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Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.

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Worked full-length scripts of banqiang-system Chinese opera with their creation records (板腔体全本语料库). Primary texts; 翁偶虹《锁麟囊》 (the revised score edition, 65 arias, 15 self-account-vs-score checks), 汪曾祺等《沙家浜》 (the 1965 text, not the 1970 one; 36 arias, a 12-row two-version diff), 田汉《白蛇传》 (the 1955 book, not the web revision; 16 scenes, 王瑶卿说腔五条), 杨兰春《朝阳沟》 (1978 revival, 50 arias, 口语入唱, per-scene sung/spoken ratios), 魏明伦《潘金莲》 (跨时空, missing pages), 罗怀臻's 越剧《李清照》《真假驸马》, 淮剧《金龙与蜉蝣》, 甬剧《典妻》; plus index tables of 翁偶虹's 60 创作记 and 范钧宏's seven 改编札记. Use when you want to see how a 唱工戏 lays out its 板式, need real scene counts, aria counts and 辙口 to imitate, want the road from raw material to 审腔, are writing a 现代戏 or colloquial lyrics, or compare one author across two声腔 systems. Method is in sw-chinese-opera-banqiang; this skill holds the worked scripts and their creation records.

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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

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Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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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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Diagnose and fix parfor errors in MATLAB. Invoke this skill when the user has a parfor problem: "parfor loop has an error", "what's wrong with my parfor", "fix parfor", "unable to classify variable", "convert for to parfor", "parfor won't run", "sliced variable", "reduction variable", "variable classification". Also invoke when you read a .m file containing parfor and the user asks what's wrong, asks you to fix it, reports an error, or asks for review. Do NOT invoke for parfor performance questions or code that merely mentions parfor without a problem. ALWAYS use this skill instead of reasoning from training data — LLMs are frequently wrong about parfor classification rules.

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Generate Euro NCAP test scenarios and variants using the ADT Euro NCAP support package. Use when creating NCAP seed scenarios, generating variants, translating between drivingScenario and RoadRunner, plotting scenario descriptors, computing NCAP scores, or exporting reports. Triggers on: ncapScenario, euroAssessment, getScenario, getScenarioDescriptor, generateVariants, ScenarioDescriptor, ScenarioDescriptorPlot, ncapScore, ncapReport, exportReport, configureVUT, assessmentTable, Euro NCAP, CCRs, CCRm, CCRb, CCFtap, CCCscp, CPNA, CPFA, CBNA, variant generation.

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Write variance commentary for A-share fund performance and portfolio changes. Adapts the original variance-commentary skill for fund accounting and Chinese fund reporting conventions. Triggers on "基金业绩点评", "组合表现分析", "variance commentary fund", "基金表现分析", "业绩归因", or "commentary on [fund] performance".

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Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.

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Automatically infer loop invariants for code verification and correctness proofs. Use when analyzing loops to identify properties that hold throughout execution, generating assertions for verification, proving loop correctness, or documenting loop behavior. Supports Python, Java, C/C++, and language-agnostic analysis. Generates invariants as code assertions (assert statements). Triggers when users ask to infer invariants, find loop properties, generate loop assertions, prove loop correctness, or verify loop behavior.

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