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概要と使いどころ
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
BigCommerce integration. Manage Products, Customers, Orders, Carts, Coupons, GiftCertificates and more. Use when the user wants to interact with BigCommerce data.
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。
AVADA Commerce integration. Manage data, records, and automate workflows. Use when the user wants to interact with AVADA Commerce data.
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。
API to every Ecommerce Cart and Marketplace integration. Manage data, records, and automate workflows. Use when the user wants to interact with API to every Ecommerce Cart and Marketplace data.
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。
Akana by Perforce integration. Manage data, records, and automate workflows. Use when the user wants to interact with Akana by Perforce data.
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。
Adobe Commerce integration. Manage data, records, and automate workflows. Use when the user wants to interact with Adobe Commerce data.
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。
ADP Workforce Now integration. Manage Persons, Organizations, Jobs, Payrolls, Benefitses, Talents. Use when the user wants to interact with ADP Workforce Now data.
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。
Query decomposition and multi-source search orchestration. Breaks natural language questions into targeted searches per source, translates queries into source-specific syntax, ranks results by relevance, and handles ambiguity and fallback strategies.
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。
Plan and optimize resource allocation. Trigger with "resource planning", "capacity", "utilization", "staffing plan", "who should work on what", "we're stretched thin", or when the user needs help allocating people, budget, or time across projects and teams.
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。
Add or modify WooCommerce backend PHP code following project conventions. Use when creating new classes, methods, hooks, or modifying existing backend code in WooCommerce projects.
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。
Guidelines for UI text and copy in WooCommerce. Use when writing user-facing text, labels, buttons, messages, or documentation in WooCommerce projects.
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。
Run tests, linting, and quality checks for WooCommerce development. Use when running tests, fixing code style, or following the development workflow in WooCommerce projects.
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。
Review WooCommerce code changes for coding standards compliance. Use when reviewing code locally, performing automated PR reviews, or checking code quality in WooCommerce projects.
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。
Portable multi-source research with cross-source validation and an offline evidence ledger. Use for fact-checking, comprehensive research, or any question requiring multiple independent perspectives; work with the host agent's search tools and optionally add SandBase Tavily, Exa, Scholar, and Cloudsway coverage.
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。
Plan, implement, or review source management work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests source management work.
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。
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.
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。
Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.
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。
This skill should be used when the user wants to query their Google NotebookLM notebooks for citation-backed, source-grounded answers, or manage notebooks, sources, and Studio content (audio, report, video, infographic, presentation, data table, flashcards, quiz, mind map). It drives the @roomi-fields/notebooklm-mcp engine — via the notebooklm MCP tools when they are available in the session, otherwise via its HTTP REST API — and covers Google login, citation formats, the daily-quota-aware batch/ingestion pattern, and source discovery.
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。
Finds orphaned, idle, and underutilized cloud resources across AWS, GCP, or Azure accounts. Use when someone needs to audit cloud spending, find unused EBS volumes, stale snapshots, unattached IPs, idle load balancers, or oversized RDS instances. Trigger words: cloud waste, orphaned resources, unused volumes, cloud audit, infrastructure cleanup, cloud bill 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。
Deploys and integrates Chatwoot, an open-source customer support platform with live chat and a shared multi-channel inbox. Use when a user asks to set up open-source customer support, add live chat without SaaS costs, build a multi-channel inbox, or deploy a free Intercom alternative.
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。
Activepieces is an open-source workflow automation platform with a visual builder, 400+ integrations ("pieces"), code steps, branching, loops, and webhooks. Use when a user asks to self-host a Zapier or n8n alternative, automate workflows with a no-code builder, build a custom integration piece, or set up open-source business automation.
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。
Building production-ready Model Context Protocol servers that expose tools, resources, and prompts to AI assistantsUse when "mcp server, model context protocol, mcp tool, mcp resource, claude integration, ai tool integration, mcp, model-context-protocol, anthropic, claude, ai-integration, tools, resources, prompts" mentioned.
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 Godot 4 game developer specializing in GDScript, the node/scene system, signals, resources, and engine-native patterns. Provides deep knowledge of Godot's unique architecture, performance optimization, and best practices for building games from simple prototypes to production-ready releases. Use when "godot, gdscript, godot 4, godot engine, build a game with godot, godot scene, godot node, godot signal, godot resource, godot tilemap, godot physics, godot animation, godot ui, godot multiplayer, godot, gdscript, game-engine, game-development, 2d-games, 3d-games, open-source" mentioned.
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 in designing and implementing intelligent game AI systems including behavior trees, finite state machines, GOAP, utility AI, pathfinding, steering behaviors, and perception systems. Specializes in creating believable, performant NPC behaviors that enhance player experience. Use when "game AI, NPC behavior, behavior tree, state machine for game, enemy AI, pathfinding, A* algorithm, navmesh, steering behavior, GOAP, utility AI, AI perception, combat AI, companion AI, boss AI, crowd simulation, flocking, game-ai, behavior-trees, pathfinding, npc, state-machines, goap, utility-ai, steering, perception" mentioned.
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。