「variants」の検索結果

Query gnomAD (Genome Aggregation Database) for population allele frequencies, gene constraint scores, and variant annotations to interpret ENCODE regulatory variants. Use when the user needs allele frequencies for variants in ENCODE regulatory elements, wants to assess gene constraint (pLI, LOEUF) for ENCODE target genes, needs population-specific frequencies for GWAS variants overlapping cCREs, wants to filter variants by rarity before functional annotation, or is interpreting ENCODE CRISPR/MPRA results in the context of population genetics. Also use when the user mentions gnomAD, allele frequency, pLI, LOEUF, constraint, rare variants, population frequency, ExAC, or variant filtering.

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Annotate genetic variants (GWAS hits, eQTLs, rare variants) with ENCODE functional data to interpret non-coding variation. Use when the user has variants of interest and wants to understand their regulatory context, identify causal variants from GWAS loci, assess variant impact on regulatory elements, perform enrichment testing of variant sets in tissue-specific annotations, or link variants to target genes through enhancer-gene maps. Handles the full post-GWAS workflow from variant set → tissue mapping → functional annotation → fine-mapping awareness → enrichment → variant-to-gene → prioritization. Use this skill for ANY variant interpretation task involving ENCODE chromatin, accessibility, TF binding, or 3D genome data.

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処理の高速化を、現状の計測、仮説ごとの変更案の比較、正しさの検証へ分解し、時間や費用の上限内で最も速い安全な案と再実行できる手順を残すスキル。

• 遅い処理を計測しながら高速化したいとき
• 同じ入力条件で実装案を比較したいとき
• 応答時間・処理量・実行費用の評価

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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新規追加された UI コンポーネントに、variants（種類）とインタラクティブ状態（hover / focus / disabled / loading / error）が定義・文書化されているかを確認し、状態設計の欠落を検出する

Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).

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Assign pathogen lineages and track variants using Nextclade and pangolin for viral surveillance. Monitor variant prevalence and identify emerging variants of concern. Use when classifying viral sequences, tracking lineage dynamics, or monitoring for variants of concern.

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Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.

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Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

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Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the somatic/trio/RNA boundary to the ClairS/Clair3-Trio family. Use when calling germline SNVs/indels from ONT or HiFi BAMs, choosing a Clair3 model, phasing variants, or benchmarking long-read calls.

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Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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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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Generate logo variants for a brand, producing multiple production-grade marks across different architectures (wordmark, lockup, symbol-only, letterform-as-symbol, monogram) with rationale, application specs, and decision-ready presentation. Distinct from `brand-identity`, which produces the complete system (logo plus color, type, voice, applications). Goes deep on the logo specifically: typographic register, symbol approach, application-context discipline (16px favicon, embroidery, signage, motion), and production specs (SVG-ready, single-color, reverse). Triggers on logo design, design a logo, wordmark, monogram, lockup, mark variants, logo iterations, redesign or refine the logo, logo refresh, letterform-as-symbol, and exploring mark variants for an established brand. Does NOT fire for full brand identity (use `brand-identity`), positioning or strategy (use `brand-discovery`), project-wide creative direction (use `creative-direction`), or one-off illustration.

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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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Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.

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The intersection of AI generation and performance marketing. This skill covers creating ad creatives at scale using AI tools—from static images to video ads to dynamic creative optimization—while maintaining the conversion focus that performance marketing demands. Traditional creative testing is slow: create 5 variants, wait weeks for data, iterate. AI-powered ad creative is fast: generate 50 variants in an hour, test simultaneously, learn in days, iterate continuously. The constraint is no longer production capacity—it's testing velocity and creative strategy. This skill bridges AI generation capability with advertising effectiveness, ensuring that AI-created ads don't just look good—they convert. Use when "AI ads, ad creative, performance creative, ad generation, creative testing, ad variants, DCO, dynamic creative, Meta ads, Google ads, ad fatigue, conversion creative, advertising, performance-marketing, creative-testing, ai-ads, conversion, paid-media, scale" mentioned.

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Find tests that enumerate a domain by copying it, and declare the invariants a codebase depends on. Reports where a parametrize list, for-loop, or it.each iterates a hand-written subset of a dict/set/tuple/Enum that exists in the source, and names the members nothing covers. Use when reviewing tests, when a module gains a name-to-thing table, registry, enum, or dispatch map, before trusting a green suite as evidence a domain is covered, or when asked "is this test actually total", "does anything cover X", "what does this repo guarantee", "which invariants do we declare". Also for vacuous tests that pass over an empty collection, for a domain that has silently NARROWED (an enumeration cannot see that), and for recording the refutation that proves a claim can fail.

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Use when a screen design needs variants - how many, one document per variant, what makes two variants genuinely different, recommending one, how the human's choice is read, and narrowing to the chosen one in revision

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Guide for annotating ENCODE regulatory variants with ClinVar clinical significance. Use when users need to check if variants in ENCODE peaks have clinical associations, find pathogenic variants in regulatory regions, or assess variant clinical impact. Trigger on: ClinVar, clinical significance, pathogenic variant, variant classification, clinical variant, disease variant, VUS, benign, likely pathogenic.

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Write paid ad copy for LinkedIn, Google, Meta, and YouTube. Produces multiple variants per platform, each targeting a different Eugene Schwartz awareness level and Cialdini persuasion principle. Use when the user asks for ad copy, ad variants, "write LinkedIn ads", Google ads, Meta ads, paid social copy, ad headlines, or wants creative for a paid campaign. Reads brand voice, ICP, and positioning from knowledge/. For organic social posts, see linkedin-post or social-calendar. For the landing page the ad points at, see landing-page-writer. For testing variants, see ab-copy-writer.

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Generate A/B copy variants grounded in Eugene Schwartz's awareness and market-sophistication stages and John Caples' tested-headline discipline. Every variant declares its angle, the awareness stage it targets, and a falsifiable hypothesis, and the skill recommends which pair to test first and what delta counts as signal. Use when the user says "A/B test this", "write variants", "give me options", "test different angles", "copy variants", "ab copy", "test this headline", "multiple versions", "which version is better", or wants to test copy before publishing. For reviewing copy that already exists, see copy-review. For sample size and stopping rules, see growth-experiment. For diagnosing a page rather than its words, see page-cro. For full paid ad sets by platform, see ad-campaign-writer.

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Use when configuring or troubleshooting the B2B/B2C Commerce product catalog — ProductCatalog, ProductCategory, ProductCategoryProduct, WebStoreCatalog, CommerceEntitlementPolicy, product attributes, and product variants. Trigger keywords: product catalog, product category, commerce catalog, entitlement policy, product variants, product attributes, catalog visibility. NOT for taxonomy, attribute strategy or category navigation design — use admin/commerce-catalog-strategy. NOT for search ranking, facets or index rebuilds — use apex/commerce-search-customization. NOT for CPQ catalogs.

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Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。