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概要と使いどころ
Model sports rules, event formats, prediction types, scoring implications, lock rules, data requirements, and provider constraints for FriendlyBet. Use when adding or comparing sports such as football, basketball, American football, tennis, tournaments, leagues, playoffs, brackets, weekly picks, or World Cup-specific logic.
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 FriendlyBet quality, regression risk, test plans, release readiness, version bump discipline, scoring/locking correctness, provider fallback behavior, and dirty-worktree safety. Use before shipping app code, scoring changes, migrations, sports integrations, copy updates, PWA cache changes, or any risky user-facing flow.
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 or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet code.
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。
Universal AI design correction rules — SUPERSEDED. The `impeccable` skill now provides 27 deterministic anti-pattern rules plus a 12-rule LLM critique pass, which are more comprehensive and runtime-enforced via the `impeccable` CLI. This file is kept for backward compatibility — the `impeccable` skill's design laws and anti-patterns take precedence.
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。
Fill surface stack map, instruction templates, and root config placeholders from provided key-value updates
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。
Build accessible UI components with React Aria Components. Use when developers mention React Aria, react-aria-components, accessible components, or need unstyled accessible primitives. Provides documentation for building custom accessible UI with hooks and components.
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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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。
Bu Flutter template'inde uctan uca yeni feature iskelesi kurar — lib/feature/<ad>/ klasor yapisi, Freezed state + Cubit, StatelessWidget ya da StatefulWidget+ViewModel view, BlocProvider yerlesimi (global state_initialize mi lokal mi), TypedGoRoute, LocaleKeys cevirileri, AppPaddings/textTheme/colorScheme token'lari ve zorunlu <feature>.md modul dokumani. Kullanici "yeni feature ekleyelim", "su sayfayi projeye gore yaz", "cubit + state + view kur", "feature iskeleti cikar", "scaffold a feature" dediginde kullan.
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。
How to upgrade `alchemy` and the `effect` constellation across this repo and keep them consistent for consumers: which packages pin what, why `effect` and every `@effect/*` companion move as one set, what breaks in a typical upgrade, and how to verify a standalone `npm install` still resolves a single `effect`. Use when asked to upgrade or bump alchemy or effect, when a deploy dies inside an alchemy provider with a `TypeError` naming a missing combinator, when `check:npm-effect-resolution` fails, or when deciding whether the alchemy patch is still needed.
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。
Monitor for API billing errors and alert the owner and admin immediately. Use when: an API billing error is detected, a peer PA reports a billing error, or during routine health checks. Handles detection, notification, and fallback model switching. Model-agnostic: works with any LLM provider (Anthropic, OpenAI, Google, etc.).
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 building, updating, or styling any monday Builders presentation, slide deck, internal pitch, kickoff doc, or HTML deliverable that should match the official Builders brand. Provides the canonical color palette, typography, gradient stroke, cover decorations, card patterns, and layout primitives sourced from the 2026 Builders Kickoff master deck. Invoke when user mentions "Builders deck", "Builders style", "match the deck", "make it on-brand", "kickoff style", or asks to design a slide/page that should look like internal monday Builders material.
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 working with Coveo — coveo enterprise search platform management including sources, indexes, query pipelines, machine learning models, usage analytics, and security providers. Covers indexing health, query performance, ML model status, content freshness, and license utilization.
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 generating cloud architecture diagrams as code — creating, modifying, or reviewing infrastructure diagrams using the Python Diagrams library. Covers AWS, Azure, GCP, Kubernetes, on-premises, and SaaS provider nodes. Generates PNG/SVG architecture diagrams from Python code with Graphviz rendering.
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 working with Cloudhealth — cloudHealth by VMware multi-cloud cost management and governance. Covers cost reporting, rightsizing recommendations, governance policies, budget tracking, reserved instance management, and compliance auditing. Use when analyzing cloud spend across providers, enforcing tagging policies, or tracking budgets.
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 working with Cert Manager — cert-manager certificate lifecycle management. Covers certificate issuance, issuer configuration, ACME challenges, certificate renewal, troubleshooting failed issuance, and trust management. Use when managing TLS certificates in Kubernetes, debugging issuance failures, configuring ACME providers, or monitoring certificate expiration.
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 working with Azure Functions Deep — deep Azure Functions analysis covering function app inventory, trigger types, execution metrics, scaling behavior, slot deployments, application insights integration, and consumption plan cost tracking. Provides optimization guidance for cold starts and memory usage.
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 working with Azure Cdn — azure CDN management covering profiles, endpoints, custom domains, caching rules, origin groups, and performance analytics. Supports Microsoft, Verizon, and Akamai CDN providers within Azure. Use when managing Azure CDN endpoints, analyzing cache performance, configuring delivery rules, or troubleshooting content delivery.
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 working with Aws Ecs — aWS ECS cluster health, service status, task analysis, capacity provider management, and deployment tracking. Covers service stability, task failure investigation, container insights, resource utilization, and scaling 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。
Use when working with Prowler — prowler cloud security assessment tool. Covers AWS, Azure, and GCP security posture assessment, CIS benchmark evaluation, compliance frameworks (PCI-DSS, HIPAA, SOC2), multi-account scanning, and remediation guidance. Use when assessing cloud security posture, running compliance audits, or investigating security findings across cloud providers.
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。
' - **Content:** The user provided a list of daily paper submissions with titles, authors, and some metadata. I need to extract the single most valuab
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GitHub - skyiron/crushAIcodingAgent: The glamourous AI coding … — The quickest way to get started is to grab an API key for your preferred provider su
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Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
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。
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB 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。
Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
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。