「dom」の検索結果

Backlink / off-page SEO audit. Works WITHOUT a paid tool by default — it uses the Google Search Console Links report (your top linking sites, most-linked pages, and anchor text) plus any backlink export the user provides (Ahrefs / Semrush / Majestic / DataForSEO CSV). Analyzes referring-domain profile, anchor-text distribution (over-optimized / spammy patterns), most-linked pages vs. money pages (internal-linking opportunity), toxic/spam link signals, and link-building priorities. Use this skill when the user asks about backlinks, link building, off-page SEO, referring domains, anchor text, toxic/spam links, disavow, or their link profile. Trigger on: "backlinks", "backlink audit", "link building", "off- page SEO", "referring domains", "anchor text", "toxic links", "spam links", "disavow file", "who links to me", "link profile", "domain authority". NOTE: rich third-party metrics (DR/DA, full link index) require a paid data source; this skill is explicit about what it can and can't see without one.

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Use this skill for hands-on DOCA GPI programming — wiring a GPU-Packet-Initiator context so a CUDA kernel drives RDMA queues directly from GPU memory without host CPU mediation. Covers picking GPI vs doca-gpunetio, the doca_gpi / domain / channel object model, the GPU-side handle handoff (doca_gpu_gpi_channel*), attaching GPU memory to a GPI domain, the domain and channel attribute objects, and debugging DOCA_ERROR_* from doca_gpi_* calls. Trigger even when the user does not explicitly mention "DOCA GPI" — implicit phrasings include "my CUDA kernel needs to post RDMA directly from GPU memory", "DOCA_ERROR_* from doca_gpi_gpu_channel_get", "how do I hand a GPU handle to my CUDA kernel", "how many channels can a GPI domain hold", or "GPU kernel driving RDMA without the host CPU on the path". Refuse and route elsewhere for the doca-gpunetio Send/Receive surface, the doca-rdma queue lifecycle, DPA-side initiation (doca-rdmi), or the CUDA programming model — those belong to other skills.

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Add a new knowledge domain to your existing system. Derives domain-specific configuration through conversation, generates domain folders, templates, and vocabulary while preserving and connecting to your existing architecture.

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Identify domains, families, and sites in proteins; find all proteins in a family or sharing a domain; explore species distribution for a domain; annotate genomes with protein families and GO terms. InterPro combines 14 databases (e.g., Pfam, CDD) into one searchable resource. InterPro-N significantly expands annotation and sequence coverage with deep learning. Includes domain architecture (IDA) search.

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Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.

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Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways.

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Deploys a Regional REST API with a custom domain name, a Lambda backend function, and a request-based Lambda authorizer using AWS CLI. Covers ACM certificate provisioning, API Gateway REST API creation, Lambda function deployment, request authorizer setup, custom domain configuration, base path mapping, and Route 53 DNS record creation. Trigger keywords: custom domain, REST API, Lambda, Route 53, API Gateway, regional endpoint, request authorizer, base path mapping.

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Detects TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not differential partition, and the insulation-vs-compartment orthogonality. Use when calling TADs or domain boundaries, computing insulation scores, choosing a window size, ranking boundary strength, comparing boundaries across conditions, or annotating CTCF-backed boundaries; route domain rendering to hic-visualization and boundary-feature overlap to genome-intervals.

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Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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snapDOM is a fast, accurate DOM-to-image capture tool that converts HTML elements into scalable SVG images. Use for capturing HTML elements, converting DOM to images (SVG, PNG, JPG, WebP), preserving styles, fonts, and pseudo-elements.

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Alias for domain-modeling. Use when the user asks for domain modeling, ubiquitous language, domain terminology, glossary work, or DDD vocabulary.

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Analyzes an existing codebase using strategic Domain-Driven Design to discover bounded contexts, surface ubiquitous language and semantic collisions, find places where code boundaries diverge from domain boundaries, and produce an evidence-backed domain and context map. Use when the goal is DDD-specific: bounded context discovery, ubiquitous language analysis, domain model discovery, or mapping contested ownership and responsibility. The entire repository is a valid scope; no module or directory must be named first. Does not assess architectural coupling, cohesion, or technical debt — use architectural-analysis. Does not design service communication, integration patterns, or deployment topology — use architectural-analysis or plan-a-feature. Does not refactor code, diagnose bugs, or review code quality — use refactor, investigate, or code-review.

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Research any domain with WHOIS data, DNS records, SSL certificate details, domain age, reputation scoring, and security header analysis. Covers ownership research, infrastructure mapping, security assessment, and competitive domain analysis.

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Audits and troubleshoots DNS records for domains including A, AAAA, CNAME, MX, TXT, SPF, DKIM, DMARC, CAA, and NS records. Use when someone needs to verify DNS configuration, debug DNS propagation issues, check email authentication records, or audit domain security. Trigger words: DNS records, dig, nslookup, SPF, DKIM, DMARC, MX records, DNS propagation, nameservers, CAA, domain configuration.

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微信私域运营 (企微 + 个人号 + 公众号 + 视频号 + 小程序 + 朋友圈 + 社群 全链路 — 引流 / 沉淀 / 触达 / 转化 / 复购 / 裂变) (WeChat private-domain operations — 微信生态(企微 + 个人号 + 公众号 + 视频号 + 小程序)的私域操盘) Master OS — automated mastery of WeChat private-domain operations — 微信生态(企微 + 个人号 + 公众号 + 视频号 + 小程序)的私域操盘: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on WeChat private-domain operations — 微信生态(企微 + 个人号 + 公众号 + 视频号 + 小程序)的私域操盘 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「私域」「私域运营」「私域操盘」「微信私域」「企业微信」

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Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, or when another skill needs to maintain the domain model.

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Domain-driven modeling patterns for Axone contracts. Use when introducing domain concepts, encoding invariants, or deciding boundaries between domain, handlers, services, gateways, queries, and state.

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Search and analyze products on the Chinese 1688 wholesale platform (Alibaba domestic B2B market) for sourcing, supplier discovery, and product selection. Trigger when the user mentions 1688 product search, finding suppliers on 1688, wholesale product queries, factory sourcing, dropshipping supplier search, 1688 keyword product selection, wholesale price comparison, filtering by sales volume, any product research on the 1688 platform, 1688 search, 1688 product selection, find suppliers, factory lookup, wholesale pricing, supplier search, domestic sourcing, 1688 products. Even if the user does not explicitly say "1688", if their need involves searching for wholesale products, finding domestic suppliers, or sourcing from the domestic Chinese market, this skill should also be triggered.

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Active Directory exploitation after domain credentials exist: Kerberos (AS-REP/Kerberoast including cracking the ticket then using the account), delegation, NTLM coercion/relay, lateral movement, ACL abuse, ADCS ESC1-ESC17 and CVE paths, dMSA/BadSuccessor, Kerberos reflection, identity confusion, management-plane, domain trust, and domain persistence. Own the current AD chain through crack-and-use to DA, equivalent domain control, or the targeted host SYSTEM. Do not stop after requesting TGS. Host C2 belongs to /post. Operator chooses next modules.

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Enumerate subdomains using passive and active techniques (subfinder, amass, chaos, assetfinder, crtsh, DNS brute force). Use when the user has a root domain in scope and needs the full subdomain list before probing.

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Build or update a CONTEXT.md domain glossary for the current project. Establishes shared vocabulary with term definitions, avoid-aliases, and relationships so agents use consistent, concise language. This skill should be used when starting a new project, when domain terminology is inconsistent across sessions, when the user says "build a glossary", "define our terms", "shared vocabulary", or "domain language", or when an existing CONTEXT.md needs updating after new domain concepts emerge.

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Communicate an integrated insight in a way that is accessible, actionable, and preserves the multi-domain nature of the understanding. Integrated insights are multi-dimensional — linearizing them risks losing the relationships that make them valuable. This skill provides a structured procedure for choosing the right form, expressing the gestalt with honest attribution, and inviting productive challenge. Use after integrate-gestalt has formed a cross-domain understanding that needs to be communicated to an audience — domain specialists, generalists, or decision-makers.

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Use when a user asks to investigate, check, or characterize a domain or hostname. Chains VirusTotal, URLScan (search existing scans), Shodan (DNS resolve + host), OTX, ransomware.live (victim-status sweep), and optionally Censys. Returns reputation, resolution, hosting fingerprint, ransomware-claim status, and pivot candidates. Invoked by /cti-orchestrator when the target is a domain.

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"Habitual Domain (HD) theory by Taiwanese scholar P.L. Yu (游伯龍, 1977) — integrates four domains (potential/actual/reachable/activated), eight general hypotheses, seven-layer decision structure, and nine HD-expansion tools to analyze decision blind spots, break cognitive inertia, and support cross-domain integration. Use for diagnosing why alternatives are invisible, decision paralysis, breaking cognitive frames, or explaining perception gaps between people. Triggers: 『思維框架』『打破盲點』『創新思考』『決策盲區』『跨域整合』『游伯龍』『習慣領域』『HD 理論』『七層決策』『四領域』『八大通性』『P.L. Yu』. Especially for NYCU EMBA HD course, 政大科管所, 中山企管系 decision-analysis/innovation/leadership courses. Taiwan-origin theory cited in J. Optimization Theory, Theory & Decision, Human Systems Management.".

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