378 件 ・ 関連度順
概要と使いどころ
Create and render fixed-canvas editable visual designs, including posters, marketing graphics, covers, menus, banners, and social cards. Default outputs include fixed and editable HTML, PNG, a mouse editor with scan and exploded layer views, and an evidence-backed Design Replay. Do not use for websites, slide decks, videos, or standalone logo systems. Always use this skill when the project contains `poster.json`.
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。
21st.dev Magic MCP — AI-powered UI component generation via natural language. Access to 21st.dev component library, SVGL brand logos, and real-time preview. Generate React/Tailwind components with /ui command.
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。
Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency analysis.
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。
Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.
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。
Detect NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log analysis.
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。
Detect Pass-the-Hash attacks by analyzing NTLM authentication patterns, identifying Type 3 logons with NTLM where Kerberos is expected, and correlating with credential dumping.
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 Windows Event Logging with advanced audit policies to generate high-fidelity security events for threat detection and forensic investigation. Use when enabling audit policies for logon events, process creation, privilege use, and object access to feed SIEM detection rules. Activates for requests involving Windows audit policy, event log configuration, security logging, or detection-oriented logging.
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 or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.
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 or edit images using the OpenAI Image API (gpt-image-2). Use when the user asks to generate, create, draw, render, illustrate, mock up, or edit an image, icon, logo, mockup, illustration, OG image, blog hero, marketing asset, or similar visual. Also use when the user supplies a reference image and asks to modify, restyle, or remix it. Triggers on: "generate an image", "create an image", "make a picture of", "edit this image", "restyle this", "make a mockup of", "draw a", "render a", "illustration of".
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 a branded PDF report with custom client logo placeholder, colors, and content sections for white-label agency delivery.
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 a personalized email banner image with text, logo, and brand colors.
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 a branded Open Graph image with a generative wave background, logo, and tagline.
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。
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM; Liu 2022 *NAR Genom Bioinform* 4:lqac046), MeTPeak (HMM over sliding windows; Cui 2016 *Bioinformatics* 32:i378), MACS3 / MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment confirmation via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output formats, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union; cross-caller IDR-equivalent), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody-based methods cannot resolve, and orthogonal-validation routes (miCLIP / GLORI / m6A-SAC-seq / m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set as a whole, reconciling differing peak sets across tools, validating MeRIP peaks against orthogonal single-base methods, interpreting peaks at the 5' end where m6Am contamination is possible, or recommending a multi-tool consensus 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。
Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.
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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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。
Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.
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。
微信公众号封面对模板 — 主图（headline + subline + 视觉锚点）+ 次图/分享图（短标题 + logo / 账号标签）+ 长图头（文章标题 + 章节预览）。比小红书封面要求更强的标题腿粗、更明确的安全区、更克制的阅读节奏。必须按当前微信尺寸读 canvas-and-device.md。触发："做个公众号封面 / 微信首图次图对 / 视频号封面 / 公众号分享图 / 长图头"。NOT XHS 3:4 尺寸，NOT XHS 贴纸密集风。
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。
Extrai o brand kit do Figma — tokens de cor, tipografia, grid, logo e componentes — e grava na seção 3 do contexto. Use quando o usuário disser "extrair a marca", "pegar os tokens", "quais as cores", "design system", "importar do Figma", "montar brand kit", ou quando a seção 3 estiver vazia. Lê do Figma; não inventa paleta.
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。
Revisa e melhora copy existente através de 7 passes focados (clareza, voz, e daí, prove, especificidade, emoção, risco zero) somados à detecção determinística de padrões de escrita de IA. Use quando o usuário disser "revisar copy", "melhorar esse texto", "revisão", "está parecendo IA", "polir", "dar uma olhada nesse texto", "feedback de copy", ou logo após qualquer skill copy-* gerar um rascunho. Não reescreve do zero — aprimora preservando a mensagem central.
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。
Transforma análise de desempenho em plano de ação priorizado por tiers — ganhos rápidos, mudanças estruturais, experimentos e o que parar de fazer. Use quando o usuário disser "o que eu faço agora", "como melhorar", "plano de ação", "por onde começo", "otimizar campanha", ou logo após perf-analyzer e perf-patterns. Fecha o ciclo: escrever → medir → aprender → escrever melhor.
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。
Create any visual design using Adobe Express templates — including flyers, posters, banners, social media posts (Instagram stories, Facebook posts, LinkedIn graphics), business cards, invitations, greeting cards, resumes, cover letters, brochures, newsletters, certificates, presentations, YouTube thumbnails, email headers, logos, menus, labels, and more. Use this skill whenever the user wants to make, design, create, or build any visual — even if they just say "make me a flyer", "design a poster", "I need something for Instagram", "create an event invite", "make a business card", or any similar request. Also handles requests to find or browse templates, edit text/copy, change background colors, or animate a design. Access: 🔐 Signed-In required | Gen AI: ❌
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。
Create handcrafted static SVG graphics and performant SVG animations — icons, illustrations, loaders, path-draw effects, morphing shapes, and motion paths. Load when the user asks to create SVG, draw an SVG icon, animate an SVG, make an SVG loader, path animation, shape morph, animated logo, line drawing effect, or improve trashy AI-generated SVG. Also triggers on "SVG animation", "SMIL", "stroke-dashoffset", "vector illustration", or "self-contained animated SVG".
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。
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM; Liu 2022 *NAR Genom Bioinform* 4:lqac046), MeTPeak (HMM over sliding windows; Cui 2016 *Bioinformatics* 32:i378), MACS3 / MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment confirmation via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output formats, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union; cross-caller IDR-equivalent), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody-based methods cannot resolve, and orthogonal-validation routes (miCLIP / GLORI / m6A-SAC-seq / m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set as a whole, reconciling differing peak sets across tools, validating MeRIP peaks against orthogonal single-base methods, interpreting peaks at the 5' end where m6Am contamination is possible, or recommending a multi-tool consensus 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。
Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.
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。