「route」の検索結果

Builds Next.js 14+ applications with App Router, server components, server actions, middleware, streaming SSR, and Vercel deployment.

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。

Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.

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。

Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck, FSx throughput, cross-AZ latency, topology mismatch.

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。

Use when the user wants to build an evaluation system for an LLM/agent application but doesn't know where to start — they have traces, prompts, RAG pipelines, or nothing at all. Also use when the user mentions evaluation, eval, benchmarking, testing LLM quality, measuring agent performance, assessing RAG accuracy, or wants to compare prompts/models. This skill is the entry router: it asks diagnostic questions then recommends which sub-skill (local workflow) to use next.

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。

Routes between five assessment pathways — formative, rubric/criteria, authentic/performance, peer/self, and diagnostic — with validity and equity checks. Use when a teacher needs help choosing how to assess.

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。

Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

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。

Use and read this skill immediately if the user request is in any way related to SEO or a site's organic search or AI search presence. That includes site audits, rankings, keyword research, competitors, backlinks, click or traffic changes, indexing problems, crawling, redirects, sitemaps, metadata, structured data, Core Web Vitals, internal links, content opportunities, programmatic SEO, local search, Search Console or Google Analytics questions, Google update impact, llms.txt, AI search visibility in ChatGPT, Claude, Perplexity, or Google AI Overviews, and client SEO reporting. Routes to evidence-backed local reports through the SEO CLI and MCP server.

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。

Pick the right LLM for LEGAL TRANSLATION — translating contracts, statutes, case law, and legal correspondence across languages, including Arabic/MENA. Vendor-neutral routing triangulated from mid-2026 evidence (WMT25 human eval, SwiLTra-Bench legal-MT, multilingual-reasoning proxies, ArabLegalEval). There is NO clean legal-translation leaderboard, so this vertical is directional and treats human legal-linguist review as mandatory. Asks up to 4 quick questions (language pair, cost, speed, privacy/certification), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to translate this contract/statute", "best AI for legal translation", "translate this legal doc to/from Arabic", or is about to machine-translate legal text without a fixed model.

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。

Post-hoc verification and trust audit of AI-generated factual and interpretive claims. Classifies claims by type and salience, routes them to domain-appropriate sources, scores trustworthiness on a tiered scale with an Interpolated verdict for plausible-but-unsupported detail, and assesses rhetorical fairness on interpretive claims. Designed for clinical-negligence and healthcare-law practice in England and Wales, with general applicability beyond.

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。

Prepare a lawyer for an adversarial proceeding the way Neal Katyal's "Harvey" prepared him for the SCOTUS tariffs argument: profile the tribunal from their prior opinions and questions, predict the specific questions you will face, map narrow "escape routes" each judge can walk through without abandoning prior commitments, and spar adversarially until only the strongest answers survive. The model is the sparring partner. The human still wins the case. Use when: preparing for oral argument, appellate argument, motion hearing, evidentiary hearing, deposition (taking or defending), arbitration, mediation, or any proceeding where a known decision-maker will fire questions at you in real time. Also useful for stress-testing a brief before filing. Proactively suggest when: the user mentions an upcoming hearing/argument with named judges or arbitrators, drafts a brief and wants it pressure-tested, asks "what will Justice X / Judge Y ask me," or talks about preparing for a specific bench.

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。

Expert EU Cyber Resilience Act (CRA) advisor for Regulation (EU) 2024/2847 — mandatory cybersecurity and vulnerability handling requirements for all products with digital elements (PDEs) sold in the EU. Use this skill for gap analysis, product classification (Default / Class I / Class II), conformity assessment route selection, CE marking, SBOM requirements, vulnerability and incident reporting to ENISA/CSIRTs, support period obligations, and manufacturer/importer/distributor duties. Trigger for EU CRA, Cyber Resilience Act, PDE compliance, Annex I requirements, SBOM EU, CE marking cybersecurity, or connected product security EU.

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。

Depth assessment of whether an AI system is high-risk under Art. 6 of the EU AI Act, grounded in the Commission's draft Art. 6(5) classification guidelines (general principles + Annex I + Annex III). Covers the Annex I product-safety route, all eight Annex III areas with worked examples, the Art. 6(3) exception and its profiling re-exception, and the Art. 25 quasi-provider trap. Outputs a structured decision block, a practitioner memo, and a JSON interchange artefact.

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 your bar comes asking "show me how you billed AI-assisted work" — and ABA 512, Florida 24-1, California, New York, and DC all have opinions out — you need an artifact that survives review. billable-time produces it. From your Claude Code session logs, it drafts reviewable time entries plus a printable HTML audit packet with: SHA-256 chain of evidence (source files + matter.yml + active disclosure pack + verifiable artifact self-hash), attorney identity and signature block, a bar-opinion disclosure pack with starter language for five jurisdictions, and content-aware deterministic narratives derived from filename and tool shape — never from prompt text by default. The tool refuses to bill on its own. --strict mode refuses to ship the artifact if any audit invariant fails (broad routes, missing attorney, missing/unverified disclosure). Comes as a Node CLI and a self-contained browser version (no backend; JSONL never leaves the page). 15 invariant tests verify the contract. AGPL-3.0.

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。

Navigation skill for genre project scaffolds: pick platformer/RPG/FPS (or other genre) then load matching template scripts and route gameplay fill to godot-genre-* skills. Use when bootstrapping architecture, bootstrap order, pause modes, PCK DLC, or feature tags — not for pasting FPS/inventory recipes. Keywords: project templates, bootstrap, Subsystem Locator, PCK, feature tags, PROCESS_MODE_ALWAYS, genre scaffold.

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。

Expert architectural standards for scalable Godot Apps, Tools, EditorPlugins, and Control-heavy UIs using Composition (Has-A Orchestrator + components). Use when building dashboards, tool windows, forms, settings panels, or EditorPlugin UIs. Do NOT use for gameplay entities (Player/Enemy/Weapon/Hitbox) — route those to godot-composition. Trigger keywords: Control, EditorPlugin, tool UI, Orchestrator, VLS, rock test, AuthComponent, ThemeManager, Saveable component, dependency injection.

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。

Expert blueprint for sports games (FIFA, NBA 2K, Rocket League, Tony Hawk) covering physics-based ball interaction, team AI formations, contextual input, and match umpire/score authority. Broadcast framing routes to godot-camera-systems. Use when building soccer, basketball, hockey, racing sports, or arcade sports games. Keywords ball physics, magnus effect, formation AI, team tactics, contextual controls, steering behaviors.

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。

Bevy genre router for tower defense. Use when placing towers, marching creeps along waypoints, and spending gold on a fixed timestep. Keywords: Bevy, tower defense, creep, waypoint, gold, ECS.

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。

AI movement decision router for chase, patrol, crowd, and bake choices on top of NavigationAgent/Server. Use when deciding node agent vs RID server, bake vs obstacle, layer masks, or retarget policy — not for engine navmesh recipes. Keywords: AI navigation, chase retarget, patrol, crowd RVO, bake vs obstacle, NavigationAgent decision tree.

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。

Expert patterns for 3D level design using GridMap with MeshLibrary, CSG constructive solid geometry, occlusion, and runtime GridMap builders. Use when building 3D levels, modular tilesets, or BSP-style geometry. For sky/fog/Environment recipes, route to godot-3d-lighting. Trigger keywords: GridMap, MeshLibrary, set_cell_item, get_cell_item, map_to_local, local_to_map, CSGCombiner3D, CSGBox3D, CSGSphere3D, CSGPolygon3D, OccluderInstance3D, bake CSG.

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。

Route a Bevy 0.20 RTS. Use when selection and move orders are components, a control group is one resource, and marching runs after the order write in one chain. Keywords: Bevy, RTS, Selected, MoveOrder, ControlGroup, chain, genre.

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。

Route a Bevy 0.20 race. Use when lap and gate index live on the racer, the race clock is one resource, and gate checks run after the clock step in one chain. Keywords: Bevy, Racing, Racer, RaceClock, FixedUpdate, chain, genre.

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。

Route a Bevy 0.20 idle clicker: one Wallet resource, generator entities, click on Update, offline bank capped in hours. Keywords: Bevy, idle, clicker, Wallet, Generator, offline, chain, ECS.

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。

Route a Bevy 0.20 platformer: coyote time and jump buffer on Mover, gravity on FixedUpdate, one-way ledges, follow camera. Keywords: Bevy, platformer, coyote, Mover, FixedUpdate, Camera2d, ECS.

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。

Route a Bevy 0.20 educational loop. Use when prompts and choices are components, lesson totals are one resource, and grading must run after selection in one chain. Keywords: Bevy, Educational, LessonProgress, Choice, chain, genre.

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。