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Analyze business concepts in a codebase to map business areas and suggest service boundaries. Use Domain-Driven Design, an approach organized around business meaning, to classify responsibilities, assess how closely they belong together, and recommend clearer integration points.
Look up a company's Trustpilot profile using its domain to collect ratings, review counts, verification status, and contact details. Optional metrics show how the business responds to negative reviews, supporting company comparisons and reputation research.
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Plan, manage, and optimize a domain portfolio. Use this skill for DNS architecture decisions, redirect strategies, registrar choice, parking unused domains, multi-site setups, and domain consolidation or split planning. Triggers on DNS, domain, registrar, redirect, parking, subdomain, apex, www vs non-www, multi-site, portfolio, hreflang setup, domain migration. Also triggers when planning a new site that needs domain decisions made before launch.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Passive domain reconnaissance using Python stdlib. Use this skill for subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required. Triggers on requests like "find subdomains", "check ssl cert", "whois lookup", "is this domain available", "bulk check these domains".
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Interactive Domain-Driven Design modeling sessions based on DDD Distilled (Vaughn Vernon) and Domain Modeling Made Functional (Scott Wlaschin). Guides users through strategic and tactical design via 13 phases: discover, storming, contexts, mapping, aggregates, events, validate, glossary, workflows, types, simulate, publish, sync. Phase 13 (sync) reconciles divergences found by --analyze between the domain model and the implementation — judging whether the model or the code is authoritative, then planning and implementing the fix and re-aligning the model. Phases 9-11 take the conceptual model from phases 1-8 and produce workflow pipeline designs, compilable type definitions (TypeScript, Kotlin, Scala, Rust, C#, F#), workflow verification via type-level tests, and automatic UI field enumeration from the domain model. Phase 12 (publish) renders all docs/domain artifacts into one self-contained, cross-linked HTML site (separate files joined by a shared nav). Each phase is an interactive dialogue where AI acts as facilitator and domain expert challenger. Use when: "DDD", "ドメイン設計", "ドメインモデリング", "Event Storming", "Bounded Context", "集約設計", "ユビキタス言語", "コンテキストマップ", "ワークフロー設計", "パイプライン設計", "ステップ分割", "中間型", "型駆動フロー", "型駆動設計", "Domain Modeling Made Functional", "Railway Oriented Programming", "入力画面項目", "フォーム項目洗い出し", "UI 項目抽出", "HTMLにまとめる", "ドキュメントサイト化", "成果物を1つのHTMLに", "publish", "モデルと実装の差異", "差異解消", "実装との乖離", "モデルとコードを一致", "差異の実装計画", "sync", "複数フェーズを逐次実行", "フェーズをまとめて実行", "/ddd run", "サブコマンド一覧", "/ddd list", "/ddd", "ドメイン分析したい", "モデリングしたい", or any domain design / type modeling activity.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
The English summary is being prepared. Showing the original description.
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Generate domain name ideas based on a project's purpose and audience, then check availability across endings such as .com, .ai, and .dev. Receive alternative names and explanations to help compare suitable options.
Build a Python system that follows DNS history, domain registration records, and certificate information from known suspicious domains or IP addresses. Map related attack infrastructure, identify shared connections, and monitor for newly discovered domains matching known patterns.
Generate domain name ideas based on a project's purpose and audience, then check availability across endings such as .com, .ai, and .dev. Receive alternative names and explanations to help compare suitable options.
Bring existing React web components and browser-only libraries into Expo apps with DOM components. Render them in a WebView, an embedded browser view, on native platforms and directly on the web to support gradual migration.
Map a website’s exposed services by collecting subdomains, active hosts, and current or historical URLs. Inspect JavaScript and exposed files, prioritize areas for security testing, and monitor new subdomains and GitHub commits.
Org-grade attack-surface mapping: given a company's legal identity, discover its ENTIRE owned internet footprint — corporate family -> owned domains -> owned netblocks/ASN -> live assets — with attribution discipline, not just DNS breadth. The org-first attribution pyramid (legal entity -> LEI/registration -> corporate family -> owned domains -> owned netblocks/ASN -> live assets). Corporate-identity resolution via the GLEIF LEI API (legal name -> LEI, exact-LEI direct-children expansion, downward-only depth-capped BFS, NEVER a name re-resolution), SEC-EDGAR full-text search + Exhibit-21 subsidiary entity names, OpenCorporates entity corroboration, Wikidata SPARQL corporate graph (P856/P355/P749/P1830). Domain attribution via reverse-WHOIS (WhoisXML preview-then-purchase quota guard, SecurityTrails associated-domains — both paid), crt.sh O= certificate-transparency organization pivot (keyless), infrastructure correlation (shared NS/MX/SaaS-TXT, netblock membership, reverse-DNS PTR), and the independent-evidence combiner (1-prod(1-w_i), rule of three, OwnerTier NONE/WEAK/MODERATE/STRONG/CONFIRMED) — discover-only `related:` candidates are NEVER auto-scanned. Netblock/ASN attribution via org-first RIR queries (ARIN Whois-RWS org-handle search, RIPE DB organisation + inverse-org search) that recover 'dark netblocks' with no DNS link to the seed, ASN discovery (RIPEstat + BGPView union), the HYPERSCALER-SCOPE GUARD (never attribute a whole AWS/GCP/Azure/Cloudflare announced range to a tenant — keep only the seed-containing block, tag shared_hosting_cdn), and org-identity-seeded internet-scan-index queries (Shodan/Censys/ZoomEye/FOFA/BinaryEdge org: filters + an always-on keyless crt.sh fallback). Promote-to-scan triage ranks forgotten discover-only netblocks by remote-access exposure (gateway-vendor/KEV/control-plane/datastore port scoring) into an operator queue. Attribution confidence rubric + anti-patterns (namesake grafting via GLEIF name re-resolution, single-signal ownership, hyperscaler over-attribution, privacy-WHOIS pivot poisoning, RIR org-name collisions). Passive/keyless-first OSINT only — every paid-key dependency is enrichment on top of a keyless core, never a hard requirement. Use when mapping an organization's full corporate-family internet footprint, resolving a legal entity to its LEI/subsidiaries, discovering domains/netblocks/ASNs an org owns beyond its one seed domain, auditing M&A/shadow-IT sprawl, or scoping an engagement that starts from a company NAME rather than a domain.
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Discuss a domain with the user, research it from real sources, then generate a trusted skill bundle for it - a step-by-step workflow with a flowchart, a domain adapter, a trap fixture, and a smoke eval. Use when the user says "/fable-domain SECTOR", "make a skill for DOMAIN", "add a domain to the fable method", or "give a lesser model Fable's workflow for DOMAIN". The bundle is the deliverable; a workflow without its flowchart, sources, and trap is not done.
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Connects a domain the user already owns (bought at another registrar) to their DropTheHassle site, free, with one A record. Use when the user says "use my own domain", "connect my domain", "add a custom domain", "point my domain to my site" or already has a .com at GoDaddy, Namecheap, Cloudflare or similar. Gets the A record from DropTheHassle, shows it to the human to set at their registrar, then checks HTTPS is live. Never edits DNS at the registrar for them. For a name they don't own yet, use buy-domain.
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Finds a free domain name for the user's website and hands them a payment link to buy it, with DropTheHassle. Use when the user wants to buy a domain, find a domain name, check if a .com is available, "get a .com" or "my own domain" for a site. Checks real availability and price with search_domain or the DropTheHassle domain search API, suggests a short list, and after a yes returns a checkout.stripe.com link the human pays. The site goes live on the name with HTTPS and no DNS. The agent never pays.
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Sets up email on the user's own domain, such as hello@yourdomain.com, a real mailbox that sends and receives, with DropTheHassle. Use when the user wants domain email, a professional email address, an inbox like info@ or hello@ on their domain, or asks "can I get email on my domain". Checks the domain is active with site_status, then hands the human a checkout.stripe.com payment link from get_email_checkout_link (main inbox €5 / $5 a month). The agent never pays and never reads the mail.
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When the user wants to plan or execute a rebrand—domain change, 301 redirects, migration, or announcement. Also use when the user mentions "rebranding," "rebrand," "domain change," "domain migration," "301 redirect," "change domain name," "rebrand announcement," "social media rebrand," "brand launch," or "domain redirect." For domain choice, use domain-selection.
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Get Ahrefs domain overview: Domain Rating, total backlinks, referring domains, organic keywords, estimated monthly traffic, and traffic value. Use when user says "Ahrefs overview", "domain rating", "DR score", "check backlinks", "how strong is this domain", "domain authority", or "check domain in Ahrefs".
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Implement randomization methods for RCTs. Use when user mentions: random assignment, treatment allocation, stratification, blocking, permutation randomization, cluster randomization, randomization balance, misfit handling, re-randomization.
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Search public TLS certificate records through crt.sh and pycrtsh to investigate lookalike domains, unexpected certificate issuers, and untracked IT assets. Compare domain spellings and review certificate details to support phishing detection and certificate monitoring.