243 skills · sorted by relevance
PXE/netboot 経由で Debian + HW RAID10 を自動インストール (OpenWrt ローカル TFTP + embed ipxe.efi + IPv4 リテラル mirror)。BMC USB redirector が累積劣化した機種 (TX1320 M3 等) や、 cross-site 拠点の install に使う。phase 19 で training-tx1320 で完遂、 物理操作なしで自力 SSH まで到達。
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Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
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Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
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This skill details how to conduct cloud security audits using Center for Internet Security benchmarks for AWS, Azure, and GCP. It covers interpreting CIS Foundations Benchmark controls, running automated assessments with tools like Prowler and ScoutSuite, remediating failed controls, and maintaining continuous compliance monitoring against CIS v5 for AWS, v4 for Azure, and v4 for GCP.
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TEKION Slide Generator v4 のブランドデザインを対話形式でカスタマイズするセットアップスキル。branding.md の Asset 層・Guideline 層（ロゴ／配色／フォント／トーン／ブランドムード／参考デザイン／禁止事項）を 1 問ずつ対話で設定し、次回のスライド生成に自動適用されるプリセットを生成する。「デザインを設定したい」「ブランドをカスタマイズしたい」「テーマを変えたい」「自社カラーに設定して」「ロゴを設定して」で発動。
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Generate Zod v4 TypeScript schemas from OpenAPI (openapi.yaml / openapi.json) definitions. This skill exists because npm packages like openapi-zod-client do not support Zod v4 — Claude reads the YAML/JSON directly and writes the conversion. Use this skill whenever the user wants to convert OpenAPI/Swagger definitions to Zod schemas, generate TypeScript types from API specs, or create validation schemas from openapi.yaml. Trigger on keywords: 'openapi', 'swagger', 'zod', 'API schema', 'yaml to types', 'schema generation', 'validation generation'. OpenAPI定義からZodスキーマを生成するスキル。openapi.yamlから型を作りたい、APIスキーマをTypeScriptの型にしたい、Zodバリデーションを作りたい、といったリクエストで使う。「openapi」「swagger」「zod」「APIスキーマ」「yamlから型」「スキーマ生成」「バリデーション生成」などのキーワードが出たら積極的にこのスキルを使うこと。
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Anime.js v4 core animations: production-level target selection, property keyframes, duration, ease, loop, play/pause. Use when implementing, reviewing or testing anime.js v4 core animations for React, Vite, Next.js or Perfect_AI projects.
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Expert en QA exploratoire v4 (tests systématiques, chasse aux bugs, preuves, rapports, régression)
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bu skill details how to conduct cloud security audits using Center for Internet Security benchmarks for AWS, Azure, and GCP. It covers interpreting CIS Foundations Benchmark controls, running automated assessments with tools like Prowler and ScoutSuite, remediating failed controls, and maintaining continuous compliance monitoring against CIS v5 for AWS, v4 for Azure, and v4 for GCP.
The English summary is being prepared. Showing the original description.
This skill details how to conduct cloud security audits using Center for Internet Security benchmarks for AWS, Azure, and GCP. It covers interpreting CIS Foundations Benchmark controls, running automated assessments with tools like Prowler and ScoutSuite, remediating failed controls, and maintaining continuous compliance monitoring against CIS v5 for AWS, v4 for Azure, and v4 for GCP.
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Zod (TypeScript-first スキーマバリデーションライブラリ) v4 リファレンス。 z.object / z.string / z.number / z.array / z.union / z.enum 等のスキーマ定義、 parse, safeParse, z.infer (型推論)、refine, transform, pipe、 default, optional, nullable、z.compile (AOT)・validate、z.toZod、 エラー処理、Zod 4 移行・Zod Mini (@zod/mini)。 createInsertSchema / createSelectSchema (drizzle-zod、Drizzle テーブルからのスキーマ生成) は別スキル drizzle。
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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
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Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
The English summary is being prepared. Showing the original description.
Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
The English summary is being prepared. Showing the original description.
Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
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react-resizable-panels v4 rules for this SSR codebase. Use when importing react-resizable-panels (PanelGroup and PanelResizeHandle are v3 names), editing components/ui/resizable* or features/code/layout/, building a split-pane, sidebar, or drag-handle layout, or persisting or toggling panel sizes.
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This skill details how to conduct cloud security audits using Center for Internet Security benchmarks for AWS, Azure, and GCP. It covers interpreting CIS Foundations Benchmark controls, running automated assessments with tools like Prowler and ScoutSuite, remediating failed controls, and maintaining continuous compliance monitoring against CIS v5 for AWS, v4 for Azure, and v4 for GCP.
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Inspect and automate macOS apps with the Peekaboo command-line tool. Capture screenshots, identify interface elements, perform clicks, typing, or keyboard actions, and check the resulting screen to verify that the intended change occurred.
Build and update Paperclip interfaces using shared rules for colors, typography, status indicators, layouts, and interactions. Reuse existing components and keep the component catalog and showcase page aligned with new or changed interface elements.
Design and build landing pages, portfolios, and website redesigns around the audience and brand. Choose suitable typography, colors, layouts, and motion, then check consistency, responsive behavior, and readable buttons and forms before delivery.
Translate and dub existing videos with HeyGen to create versions in multiple languages. Preserve the speaker’s voice characteristics, match lip movements to translated speech, or choose audio-only translation without adjusting the video’s lip movements.
Retrieve current documentation and code examples from Context7 for libraries, frameworks, and developer tools. Use the results to answer questions about API syntax, configuration, installation, version migration, and library-specific problems.
Search the 1000 Genomes Project’s 3,202 participants for genetic variants and their carriers. Filter by frequency or annotations, inspect variants in selected individuals, and check relatedness between two participants.