75 件 ・ 関連度順
概要と使いどころ
Render a target GameObject from a chosen camera angle with optional layer-based isolation, configurable background (solid/skybox/transparent), multi-light setup via JSON, and Composite (2x2 Front/Right/Back/Top) mode. Returns a PNG image. When isolated=true, inactive children may briefly fire OnEnable — see the body for side-effect notes.
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Destroy a GameObject (and all nested children) in the currently opened Prefab or active Scene. Returns the destroyed GameObject's name, path, and instance ID for confirmation. Use 'gameobject-find' to locate the target first.
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Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.
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A playful, energetic, cartoonesque interface inspired by friendly children's-book illustrations — warm cream backgrounds, big bold custom display typography, saturated brand color blocks, thick black outlines, generously rounded shapes
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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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Judges whether material for children under six is developmentally plausible — what is typical at each age, why pre-K is not a thinner kindergarten, and which frameworks apply where Common Core does not. Use this to design or review pre-K material, decide whether a task is reasonable for an age, choose the right framework for a pre-K alignment claim, or push back on material that pushes school-age expectations downward.
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Writing screenplays in the style of Ozu Yasujiro and Noda Kogo (小津安二郎・野田高梧) — distilled from six complete screenplays (晚春 Late Spring, 麦秋 Early Summer, 东京物语 Tokyo Story, 浮草 Floating Weeds, 秋日和 Late Autumn, 秋刀鱼之味 An Autumn Afternoon). Covers the "family member leaves" master plot, off-screen events / on-screen reactions, lie-driven plots, minimal-syllable confessions, go-between characters, faceless suitors, pillow-shot scene breaks, echoing objects, mirror characters, children's subplots as metaphor, displaced quarrels, the single-monologue rule, diminishing endings, Ozu's script format and stage-direction vocabulary. Use when the user wants to write a quiet family drama, a 小津风 / 侘寂 / 日常系 script, a story about marrying off a daughter, aging parents, or wants dialogue that says less and means more.
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Create child communication safety experiences using PermissionKit to request parental permission for children. Use when building apps that involve child-to-contact communication, need to check communication limits, request parent/guardian approval, or handle permission responses for minors.
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Discover OPC UA servers, connect MATLAB clients, and browse/navigate server nodes using opcuaserverinfo, the Local Discovery Service (LDS), opcua, connect, setSecurityModel, certificate-trust functions, findNodeById, opcuanode, and Namespace/Children traversal. Use when finding OPC UA servers on the network, querying endpoints and security policies, connecting to a server, authenticating with username/password or certificates, configuring security modes, handling certificate-trust or hostname-mismatch errors, troubleshooting failed connections or empty discovery, inspecting an OPC UA certificate (.der/.pem), browsing an address space, finding nodes by name or NodeId, navigating node hierarchies, invoking method nodes, or batch-processing large namespaces. Trigger on: opcuaserverinfo, LDS, opcua, opc.ua.Client, setSecurityModel, opc.ua.trustServerCertificate, OPC UA certificate, findNodeById, opcuanode, browse OPC UA, find node, OPC UA namespace, OPC UA method node, Industrial Communication Toolbox.
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Use when a story must unfold across a sequence of Scenario images: a comic page or strip, manga or webtoon panels, a children's storybook, a film, game, or ad storyboard, animatic pre-viz frames, or any shot list where the same characters and world must persist from panel to panel and dialogue must stay legible. Keywords: storyboard, comic, panel, sequential art, storybook, shot list, pre-viz, animatic, scene to scene, character consistency.
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GDPR Legitimate Interest Assessment (LIA) — Guided assessment for Art. 6(1)(f) GDPR using the EDPB three-step test. Based on EDPB Guidelines 1/2024, EDPB Opinion 28/2024 (AI models), the OSS Case Digest on Legitimate Interest (McIntyre 2026), CNIL AI guidance (June 2025), ICO guidance, and key CJEU case law. Use when: (1) user needs to assess whether legitimate interest is appropriate, (2) user asks about Art. 6(1)(f), "Interessenabwägung", "berechtigtes Interesse", "balancing test", or "LIA", (3) user wants to document a legitimate interest assessment, (4) user mentions the three-step test, (5) user evaluates necessity or proportionality under Art. 6(1)(f), (6) user discusses direct marketing, fraud prevention, IT security, employee monitoring, AI training, web scraping, or credit checks in context of legal basis, (7) user handles a right to object (Art. 21), (8) user asks about children's data, profiling, or cross-border data sharing under legitimate interest, (9) user compares legitimate interest against other legal bases. Do NOT use for: consent assessments (Art. 6(1)(a)), contract performance (Art. 6(1)(b)), DPIA workflows, or pure ePrivacy/cookie compliance questions — though the skill does address the ePrivacy/GDPR overlap where relevant.
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AI co-counsel for divorce and family-law attorneys — a jurisdiction-portable scaffold spanning the full matter lifecycle. Eight operating modes mirror how a matter actually moves: intake and onboarding, financial disclosure, children and support, property division and QDRO, discovery and document review, drafting, negotiation and mediation prep, and court prep — plus post-judgment modification and enforcement. The methodology is jurisdiction-agnostic: it forces real, verifiable research for every local form, formula, or rule rather than inventing one, handling common-law, community-property, civil-law, and MENA personal-status regimes as variables. Built around one non-negotiable: privilege. It drafts, analyzes, organizes, and pressure-tests. It is not the lawyer.
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Design a pedagogical documentation protocol for making children's learning processes visible and shareable. Use when documenting learning, communicating with families, or planning next steps.
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Plan and execute substantial multi-file builds, features, migrations, and refactors with Astra as orchestrator and DeepSeek V4.1 Flash as the native Codex implementation worker. Use for phased planning and delegated build execution, including existing Superpowers or GSD plans. Skip trivial edits and explicitly single-agent tasks. Worker children must not invoke this orchestration skill.
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Judge and shape UI/UX for products used by parents and young children - audience fit, accessibility, safety and trust, comfort, consistency, and human feel. Use when reviewing or designing family, parenting, or kids app screens on phone or tablet.
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Share state between components that don't have a direct parent-child parameter relationship, using cascading values, scoped services with change events, or CascadingValueSource via DI. USE WHEN the user needs a CascadingParameter or CascadingValue that works across render mode boundaries, a shopping cart or notification count accessible from multiple pages, a theme or user preference cascaded app-wide, or when components in different parts of the tree must react when shared data changes. Also USE WHEN cascading values aren't reaching interactive children in per-page interactivity mode, or when the user needs to understand scoped vs singleton service lifetime for state on Blazor Server. DO NOT USE for direct parent-child parameter passing or EventCallback (see author-component), for persisting state across prerender-to-interactive transitions (see support-prerendering), or for service abstractions for data fetching in Auto/WebAssembly (see fetch-and-send-data).
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Build reusable, accessible React components with predictable APIs and ref forwarding. Use when creating or refactoring components or defining component APIs, favoring composition over configuration and slots/children over many boolean props. Not needed for style-only or content-only edits that do not change a component API.
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Assesses children's data protection in educational technology. Covers COPPA school exception under Section 312.5(c)(4), FERPA intersection, parental rights, teacher consent authority, data deletion at year-end, and Student Privacy Pledge compliance. Keywords: edtech, COPPA school exception, FERPA, student privacy, teacher consent, educational data.
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Chooses how to launch subagents from Claude Code (the Agent tool, a Workflow script, or headless claude -p children) and runs a headless fan-out from one background command. Use for "fan this out to 50 agents", "run these tasks in parallel", "launch subagents for each file", "should I spawn agents or write a workflow", "batch this through Haiku", "run claude -p in parallel", "dispatch a task list without the launches eating my context", or before any launch of more than a handful of subagents, where the launching itself is the cost. Covers the parent-turn cost of each mechanism, concurrency ceilings, what context and tools each child gets, effort control, output contracts, a cost ledger, and resume after interruption.
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。
Design a pedagogical documentation protocol for making children's learning processes visible and shareable. Use when documenting learning, communicating with families, or planning next steps.
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。
Conduct Fundamental Rights Impact Assessments (FRIA) for high-risk AI systems as required by EU AI Act Article 27. Use when (1) deployers of high-risk AI systems (Annex III) need to assess fundamental rights impacts before first use, (2) public bodies or private entities providing public services are deploying AI systems, (3) credit scoring or insurance risk assessment AI systems are being deployed (Annex III 5b, 5c), (4) evaluating impact on natural persons' fundamental rights (dignity, liberty, privacy, data protection, non-discrimination, children's rights, etc.), or (5) creating EU AI Act Article 27 compliance documentation with market surveillance notification.
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The pedagogy of feeding — how to talk about food with children, what the Division of Responsibility model says, age-appropriate autonomy in eating, how to handle picky eating without creating disordered eating, and how to teach nutrition concepts to learners at different levels without either moralizing or oversimplifying. Use when a question is about raising, teaching, or advising a child or learner on eating, or when planning curriculum or parent guidance.
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Teaching home-economics skills as a durable practice across generations and capacity levels. Covers scaffolded task sequencing, the one-in-one-out rule, the apprenticeship model, learning by failure tolerance, family retros, the edible schoolyard framework, and the difference between teaching a recipe and teaching a kitchen. Use when designing a home-ec curriculum for children or adults, diagnosing why a taught skill is not being practiced, or building a household practice that survives the departure of the teacher.
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Applying motion study and time analysis to household work. Based directly on Lillian Gilbreth's *The Home-Maker and Her Job* and *Cheaper by the Dozen*, this skill covers the therblig-motion catalog, task decomposition, batch processing, parallelism, routine chart design, and the ergonomics of work surfaces. Use when diagnosing why a household is running out of time, designing a weekly routine, teaching task sharing to children, or reducing the friction of any repeated household task.
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