「aria」の検索結果

Read, query, and write WooCommerce product variations correctly — the WC_Product_Variable (parent) vs WC_Product_Variation (child) class split, the parent's get_variation_prices( $for_display ) cached aggregation and how to bust the wc_var_prices_<id> transient, programmatic variation creation via WC_Product_Variation + set_parent_id + set_attributes + save followed by WC_Product_Variable::sync, the inherited-vs-own variation stock model (parent flag, variation flag, derived stock_status), get_available_variations for frontend variation data, and the $for_display tax-handling parameter that breaks display logic when forgotten. Use when scaffolding plugin code that creates / queries / modifies variations or when debugging stale variation data after programmatic writes. Triggers on WC_Product_Variation, WC_Product_Variable, get_variation_prices, get_available_variations, product_variation post type, wc_var_prices, sync_variation, or programmatic variation creation context.

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。

Use this skill when the user asks about ARIA, ARIA roles, ARIA attributes, aria-label, aria-labelledby, aria-describedby, aria-expanded, aria-hidden, aria-live, aria-required, aria-invalid, "WAI-ARIA", "ARIA Authoring Practices", "APG pattern", "accessible name", "accessible description", live regions, custom widgets, dialogs, menus, tabs, accordions, comboboxes, tooltips, tree views, carousels, or is building any custom interactive component that requires ARIA roles and keyboard behavior beyond native HTML elements.

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。

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.

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。

Mutate WooCommerce variation prices via filters — pick the right layer (woocommerce_product_get_price for simple/non-variation products, woocommerce_product_variation_get_price for variations, the woocommerce_variation_prices_* family for the parent min/max aggregation that feeds the catalog "From X to Y" range), and the critical step of filtering woocommerce_get_variation_prices_hash whenever your price logic depends on context outside the default cache key (user role, custom toggle, time of day) — otherwise one user's filtered price gets cached and served to everyone. Use for role-based pricing, time-based discounts, or "filter variation prices but the min/max range doesn't update" debugging. Triggers on woocommerce_product_get_price, woocommerce_product_variation_get_price, woocommerce_variation_prices_*, woocommerce_get_variation_prices_hash, wc_var_prices.

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。

Configures environment variables for Power Pages site settings to support ALM across environments. Creates environment variable definitions in Dataverse, guides the user through linking site settings to those variables via the Power Pages Management app, adds the variables to the solution, and generates a deployment-settings.json file with per-stage override values. Use when asked to: "configure environment variables", "add env vars", "set up deployment variables", "make site settings environment-specific", "configure ALM variables", "set up env-specific settings", "add deployment settings", "configure per-environment settings".

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。

Use when working with icons in any project. Search 340k+ icons, add them as source files (no giant dependency), migrate existing icon libraries, and run as an MCP server. Commands: `aria-icons search`, `aria-icons add`, `aria-icons get`, `aria-icons migrate`, `aria-icons doctor`.

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。

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.

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。

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.

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。

This skill should be used when the user wants to "stress-test a design", "challenge an idea", "red team a proposal", "get critical feedback on a design", "validate a design", "adversarial architecture review", "risk assessment", "設計を検証して", "反論をもらいたい", "設計を批判的にレビュー", "デビルズアドボケート", "ストレステスト", "弱点を指摘して", "穴を見つけて", "この設計で良いか", "リスク評価", "設計のアーキテクチャレビュー", or mentions structured adversarial review of a proposal or design. NOTE: Use this for validating PROPOSALS and DESIGNS through adversarial debate, NOT for generic code review, PR review, or normal review requests (use /codex:review or /codex:adversarial-review for those). Also NOT for investigating unknown bugs (use /agent-dialectics:strong-inference for that).

MySQLとMariaDBのテーブル設計や遅いSQLを見直し、インデックス、更新処理、接続管理、複製の遅延を点検して、修正案と検証手順をまとめるスキル。

• テーブルとインデックスの設計
• 遅いSQLやデッドロックの調査
• 大規模な本番テーブルの変更レビュー

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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。

Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards. USE THIS SKILL WHEN: - You need to review or audit actuarial models (reserving, pricing, capital) - Someone asks about loss triangle analysis or reserve adequacy - You are evaluating IBNR calculations, chain ladder methods, or Bornhuetter-Ferguson - A project involves insurance pricing, GLM rating models, or ratemaking - You need to assess Solvency II SCR calculations or RBC compliance - Someone mentions actuarial opinions, ASOP compliance, or SOA standards - You are reviewing stochastic models, ESG configurations, or DFA frameworks - A codebase uses actuarial libraries (chainladder, lifetables, ChainLadder R package) TRIGGER PHRASES: "actuarial", "loss reserving", "IBNR", "chain ladder", "premium pricing", "mortality table", "Solvency II", "capital adequacy", "ratemaking", "GLM pricing", "risk-based capital", "reserve analysis", "actuarial opinion"

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。

Analyze budget vs. actual variances, identify root causes, and recommend corrective actions. Use when an accountant or finance professional says "explain this variance", "budget vs. actual analysis", "why did we miss budget", "favorable / unfavorable variance", "price volume variance", "management reporting commentary", "explain the P&L variance to the CFO", "what drove the cost overrun", "revenue shortfall analysis", or needs to write the narrative behind a financial result. Also trigger when someone shares a table of actuals vs. budget and needs to explain the gaps - even if they don't use the word "variance".

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。

標準原価差異分析スキル。標準原価（予定原価）と実際原価の差異を価格差異・数量差異に分解し、 材料費・労務費・製造間接費のカテゴリ別に集計・分析する。差異の有利/不利判定、 責任部門の特定、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: 製造原価の差異分析を行いたいとき。標準原価計算制度の運用、 月次原価差異レポート作成、原価低減活動の効果測定に使用。 Triggers: "標準原価", "原価差異", "価格差異", "数量差異", "standard cost", "cost variance", "price variance", "quantity variance", "予定原価", "操業度差異", "原価管理"

Turns an actuals-versus-budget extract (spreadsheet, export or pasted table) into a DRAFT variance table with absolute and percentage variances, favourable or adverse marking and a ranking by size, then drafts plain-language driver hypotheses for each material variance as questions to verify with a named owner, never as asserted causes. Use when the user asks to "explain these variances", "why are we over budget on this line", "write the variance commentary for the management pack", "actuals versus budget analysis" or "which lines drive the variance". Do not use for a dataset with no budget or comparison column, use dataset-insight-pack instead; for weekly operational figures against last week, use kpi-weekly-report-writer. Drafts for human review; never approves, authorises or signs off.

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 Coval adversarial / red-team testing workflow. Builds one adversarial test set (12 core attack vectors plus legitimate controls, each with an expected-behavior checklist), creates a persistent "Adversarial User" persona and a Composite Evaluation metric that scores each scenario against its own expected behaviors, launches a multi-iteration run against the agent (voice or chat), polls for completion, builds a per-scenario pass/fail scorecard, and creates a saved report grouped by Test Case. Use when a user wants to follow the Adversarial & Red-Team Testing cookbook (https://docs.coval.dev/guides/adversarial-red-team-testing) without doing each step by hand. Triggers: "adversarial test set", "red team my agent", "jailbreak / prompt-injection testing", "test my agent against bad actors".

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。

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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。

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.

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。

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

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。

Post a comment (or a formal PR review) to a GitHub issue or pull request in mariadb-operator/mariadb-operator. Use whenever the user wants to publish something to GitHub for this repo — "comment on issue #123", "post this as a PR comment", "leave a note on #456", "reply on the PR" — and especially when they want the output of the mariadb-operator-pr-review skill delivered to GitHub instead of just shown in chat, e.g. "review PR #1234 and post it as a comment" or "post the review results to the PR". Always confirms the exact comment text with the user before publishing — never posts autonomously.

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。

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.

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。

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.

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。

Unity Prefab / Scene / Asset の壊れた参照・Variant override・配線ミスを構造化応答で診断し、 dry-run → confirm 経路で安全に修復するための MCP ツールリファレンス。 Editor Bridge のセットアップ手順もここに含む。 トリガー: .prefab, .unity, .asset, .mat, Prefab, Variant, 配線, 参照, 壊れた参照, broken reference, GUID, fileID, override, パッチ, patch plan, validate refs, validate structure, where-used, inspect variant, inspect wiring, inspect hierarchy, wiring, null参照, null reference, MonoBehaviour, 階層, hierarchy, Transform整合, 孤立, orphan, 重複fileID, duplicate fileID, Editor Bridge, Bridge セットアップ, bridge setup, deploy_bridge, UNITYTOOL_BRIDGE_WATCH_DIR, watch ディレクトリ のいずれかに該当する作業で使用する。

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。