1,235 件 ・ 関連度順
概要と使いどころ
NVIDIA Sync デスクトップアプリのリファレンス。DGX Spark, DGX Station, 汎用 SSH Linux ホストへのリモート接続管理。mDNS 自動検出, 手動 IP 登録, SSH config インポート, Tailscale 連携, 自動ネットワーク切替。 Cluster Assistant, ConnectX-7 クラスタトポロジ, netplan 設定検証。 VS Code, Cursor, VS Code Insiders, NVIDIA AI Workbench 起動, カスタムアプリ登録, ポートフォワード, SSH トンネル。
NVIDIA DGX Spark User Guide リファレンス。GB10 Grace Blackwell Superchip ハードウェア仕様、first-boot / UEFI 設定、Spark Stacking クラスタ、 DGX OS / NGC / container runtime ソフトウェアスタック、 PXE / cloud-init プロビジョニング、fleet lifecycle 運用、 apt / fwupdmgr 更新、system recovery、PSIRT サポート、 vLLM / TensorRT-LLM / Ollama / ComfyUI / NIM / NeMo fine-tune / NCCL 等の build.nvidia.com/spark playbook、複数 Spark 接続・Tailscale・VS Code、 NVIDIA Developer Forums 由来の known issues / troubleshooting。
Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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。
Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.
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。
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
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。
Aumenta resolução e qualidade de imagem via Freepik/Magnific, ou orienta o processo manual quando não há chave. Use quando o usuário disser "upscale", "melhorar a resolução", "a imagem está pixelada", "Magnific", "Freepik", "aumentar a imagem", "imagem pequena demais". Gasta crédito — exige confirmação.
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。
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone needs to produce ad copy at scale or iterate on existing ads. For campaign strategy and targeting, see paid-ads. For landing page copy, see copywriting.
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。
When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," "building many pages for SEO," "pSEO," "generate 100 pages," "data-driven pages," or "templated landing pages." Use this whenever someone wants to create many similar pages targeting different keywords or locations. For auditing existing SEO issues, see seo-audit. For content strategy planning, see content-strategy.
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。
Generate archetype-driven semantic design tokens (colors, typography, spacing, radius, motion, elevation) that don't look vibecoded. Hard-bans Tailwind-default palettes, Inter-only typography, and purple→pink gradients unless the chosen archetype explicitly demands them. Load when the user asks to generate design tokens, create a design system, set up CSS custom properties, build a token scale, design a color system, set up typography scale, or when frontend-design routes here during token generation. Also triggers on "design tokens for", "token system", "CSS variables for design", "set up a theme", "build a design system foundation". Sub-skill of frontend-design. Always reads ARCHETYPE.md first.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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。
Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.
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。
Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.
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。
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
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。
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", or "data-driven SEO".
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。
Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.
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。
Improve UI typography by fixing font choices, type scale, hierarchy, weight, and readability so text feels intentional. Use when the user mentions fonts, typography, type scale, readability, text hierarchy, or sizing that feels 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。
Plan and blueprint programmatic SEO page strategies at scale
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。
機能追加や不具合修正などの開発を、規模に応じて調査・計画・テスト・レビューへ振り分けます。計画承認とコミット確認を組み込む、関連スキル共通の手順です。
複数のEVMチェーンでトークン残高や価格の桁がずれる問題を防ぐため、小数桁数の実行時取得、チェーン別キャッシュ、精度を保つ計算と単位統一を扱うスキル。
ClickHouseの分析用テーブル設計、集計クエリの作成、遅い処理の見直しを支援します。大量データの一括取り込みやリアルタイム集計の設計にも使えます。
Blenderのキャラクターやアニメーションを、骨格・座標・接地状態などのデータと画像で検査し、足滑り、向きの不一致、ねじれの原因と修正点を整理するスキル。
Pretrain LLMs at scale with PyTorch 4D parallelism.
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。
Fast vector similarity search at billion scale.
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。