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Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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。

Send a box of Krispy Kreme donuts (doughnuts) to someone by mail, paid with your own credit card via Stripe Link — driven entirely from the terminal. Use when the user wants to send donuts, doughnuts, treats, or a thank-you gift to a lead, prospect, customer, colleague, or friend — e.g. "send donuts to Jane at Acme", "mail a dozen glazed to our new customer", "send a thank-you gift to the team". Orders are placed and shipped through the Zinc API; payment is a Stripe Link Shared Payment Token via the create-payment-credential (link-cli) skill.

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。

Send a box of Krispy Kreme donuts (doughnuts) to someone by mail, paid with your own credit card via Stripe Link — driven entirely from the terminal. Use when the user wants to send donuts, doughnuts, treats, or a thank-you gift to a lead, prospect, customer, colleague, or friend — e.g. "send donuts to Jane at Acme", "mail a dozen glazed to our new customer", "send a thank-you gift to the team". Orders are placed and shipped through the Zinc API; payment is a Stripe Link Shared Payment Token via the create-payment-credential (link-cli) skill.

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。

Enforces error handling standards: silent error prohibition, custom error class hierarchy (AppError base with category never-fallback / transient / permanent, cause preservation, ValidationError / UnauthorizedError / UpstreamError etc. — canonical definitions in PATTERNS.md), external-boundary normalization (normalizeExternalError), Result pattern (Result.ok/Result.fail), proper try-catch with error type checking, structured error logging with a single Logger contract, fallback prohibition categories (isNeverFallback), and HTTP status code mapping. Use when implementing error handling, reviewing catch blocks, or designing error responses.

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 professional, visually excellent presentation slides in Marp Markdown format. Use this skill when the user asks to: - Create a presentation, slides, deck, slideshow, or プレゼン / スライド - Write or generate slides about a specific topic - Make a Marp .md file for presenting Produces complete, ready-to-render .md files with embedded custom CSS theme, narrative structure, and polished visual design. Supports Japanese and English content.

Use when working with Coderabbit — codeRabbit AI code review management including automated review comments, auto-suggestions, review customization, and learning from feedback. Use when configuring CodeRabbit for AI-assisted code review, custom review rules, and automated suggestion workflows.

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 Policy Deep — azure Policy deep compliance and governance management. Covers policy definitions, initiatives, assignments, compliance evaluation, exemptions, remediation tasks, custom policies, and regulatory compliance. Use when managing Azure governance at scale, reviewing compliance states, creating custom policies, or troubleshooting policy evaluation results.

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 Checkov — checkov infrastructure-as-code security scanning. Covers Terraform, CloudFormation, Kubernetes, and Dockerfile scanning, policy management, custom checks, compliance frameworks, and suppression management. Use when scanning IaC for security misconfigurations, evaluating compliance, managing custom policies, or reviewing scan results.

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 Semgrep — semgrep static application security testing (SAST). Covers code scanning, custom rule creation, CI integration, autofix capabilities, multi-language support, and vulnerability triage. Use when scanning source code for security vulnerabilities, creating custom detection rules, or integrating SAST into CI pipelines.

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 Tfsec — tfsec Terraform security scanning. Covers static analysis of Terraform code, custom rule creation, CI integration, severity filtering, module scanning, and remediation guidance. Use when scanning Terraform configurations for security issues, creating custom security rules, or integrating security checks into CI pipelines.

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。

Configure Netlify projects via netlify.toml and the _headers/_redirects files — covering build settings and deploy contexts alongside environment variables/scopes and the Secrets Controller plus redirect/rewrite/proxy and custom-header rules. Use when setting a build command or publish directory, adding redirect or rewrite or proxy rules, configuring custom headers or basic auth, setting or scoping environment variables and secrets, wiring up a monorepo or SPA fallback, or skipping unnecessary builds. Reach for this whenever you touch netlify.toml or ask "why is my env var undefined in a function" or "how do I redirect this path".

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。

Detects and removes infostealer malware (credential stealers, data exfiltrators) via full-system file search, cryptographic hashing, and public threat-intelligence cross-checks (VirusTotal, MalwareBazaar). Primary method is always custom hash-based detection. Windows Defender (or any platform-native AV) is allowed **only when necessary** (e.g. inconclusive hashes or deep remediation) and **must never be the default option**. The agent must exhaust the custom workflow first. Works on Windows/macOS/Linux.

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。

Orientation for custom (non-OpenAI) models running in the Codex app through the codex-router proxy. Explains that the app's native tools arrive as flattened codex_app__ and mcp__ names, that the router restores them so the app executes them, which companion skills to read before threads, browser, or computer-use work, and that a turn with no tool call ends the task. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, when codex_app__ or mcp__ tool names appear in the tool list, when a tool result just arrived and more work remains, or when thread, browser, or computer-use work is requested.

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 en parcours client (mapping, touchpoints, pain points, optimization, moments of truth)

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 en ComfyUI workflows avancé (install, custom nodes, model management, workflow design)

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 en n8n avancé (workflows, nodes, expressions, webhooks, self-hosted, custom nodes, scaling, error handling)

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。

Canonical patterns for writing custom MSBuild targets. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing custom target authoring anti-patterns, reviewing MSBuild target definitions for correctness, diagnosing broken SDK target chains across files (e.g., Directory.Build.targets silently redefining SDK targets), fixing targets that replace CompileDependsOn instead of extending it with $(CompileDependsOn), fixing query targets that return stale results due to Outputs vs Returns misuse, fixing missing Inputs/Outputs causing unnecessary rebuilds, fixing missing FileWrites registration. Covers DependsOnTargets vs BeforeTargets vs AfterTargets, the Build→CoreBuild three-level pattern, hooking into the build pipeline, the $(XxxDependsOn) chain-extension pattern. DO NOT USE FOR: incremental build tuning (use incremental-build), parallelization (use build-parallelism), general anti-patterns (use msbuild-antipatterns), non-MSBuild build systems.

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。

Generate Japanese-style (和風) CSS/design themes with CSS custom properties and Tailwind CSS support. 10 preset themes including 桜, 藍染, 紅葉, 雪月花, 茶室, 浮世絵, 禅, 祭, 未来, 和紙. Triggers on requests for 和風テーマ, 和風CSS, Japanese theme, 和風デザイントークン, テーマ生成.

Implement, review, or improve SwiftUI animations and transitions. Use when adding explicit animations with withAnimation, configuring implicit animations with .animation(_:body:) or .animation(_:value:), configuring spring animations (.smooth, .snappy, .bouncy), building phase or keyframe animations with PhaseAnimator/KeyframeAnimator, creating hero transitions with matchedGeometryEffect or matchedTransitionSource, adding SF Symbol effects (bounce, pulse, variableColor, breathe, rotate, wiggle), implementing custom Transition or CustomAnimation types, or ensuring animations respect accessibilityReduceMotion.

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。

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

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。

Guide for defining RL environments — the Env protocol, EnvGroupBuilder, RLDataset, and custom environment creation. Use when the user asks about RL environments, reward functions, or how to define custom tasks for RL training.

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。

Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.

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 Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when: segment, analytics.js, customer data platform, cdp, tracking plan.

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 custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.

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。