「with」の検索結果

xUnit, Moq/NSubstitute, FluentAssertions, and coverlet patterns for any .NET project. Covers test structure ([Fact]/[Theory]/[InlineData]), mocking discipline, fluent assertions, integration test setup with WebApplicationFactory, and coverage measurement. Stack-agnostic — referenced by every .NET plugin in the marketplace. Use this skill to: - Write clear, maintainable unit tests with xUnit [Fact] and [Theory]. - Mock dependencies with Moq or NSubstitute without overusing mocks. - Write expressive assertions with FluentAssertions. - Measure coverage with coverlet and enforce a minimum threshold. Do NOT use this skill for: - ASP.NET Core-specific integration tests (WebApplicationFactory, HttpClient — those are in aspnet-core-plugin:aspnet-conventions). - EF Core in-memory or SQL Server LocalDB test patterns (aspnet-core-plugin:efcore-patterns). - C# language idioms — see csharp-foundation:csharp-conventions.

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。

JUnit 5, Mockito, AssertJ, and Testcontainers patterns for any JVM project. Covers test structure, parameterised tests, mocking discipline, fluent assertions, and integration testing with real containers. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write clear, maintainable unit tests with JUnit 5. - Mock dependencies with Mockito without overusing mocks. - Write expressive assertions with AssertJ. - Spin up real infrastructure (DBs, message brokers) with Testcontainers for integration tests. Do NOT use this skill for: - Spring-specific test slices (@SpringBootTest, @WebMvcTest, @DataJpaTest — those are in spring-boot-plugin skills). - Framework-specific mocking utilities (MockMvc, WebTestClient).

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。

Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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。

Creates Pulumi infrastructure-as-code projects in Go, configures OIDC authentication, integrates with Pulumi ESC for centralized secrets and configuration management, and builds multi-language component resources. Use when setting up Pulumi Go projects, writing infrastructure code with Go, configuring OIDC for Pulumi, using Pulumi ESC with Go, automating cloud infrastructure with Golang, creating reusable Pulumi components in Go, or working with pulumi-go-provider. Also use when the user mentions Pulumi with Go/Golang, AWS/Azure/GCP infrastructure in Go, or Go-based ComponentResource patterns.

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。

Autonomous goal loop — give it ONE goal plus how to verify it, the agent runs check→fix→check itself and comes back only with the finished result (code with audit, research with fact-check, content with critique, audit/backtest). Self-verification instead of constant supervision, with hard safety guards. Vary the loop with a verification rubric designed before it runs. Triggers — "work autonomously to the result", "don't come back until it's done", "run the loop yourself", "drive it to done".

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。

Implements declarative Flutter routing using GoRouter v17 with URL-based navigation, deep linking, and authentication guards. Activates when configuring GoRoute path patterns, setting up StatefulShellRoute for persistent bottom navigation state, implementing ShellRoute for shared nested layouts, adding redirect guards with loop prevention (idempotent redirects), configuring deep links for iOS (FlutterDeepLinkingEnabled) or Android (intent-filters), debugging redirect loops or navigation stack issues, handling 404 error routes with errorBuilder, using named routes with path/query/extra parameters, or migrating from imperative Navigator.push to declarative routing. Ideal for Flutter web apps requiring URL bar synchronization, mobile apps with universal links or app links, or multi-level navigation hierarchies.

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。

Builds React Native and Expo apps in TypeScript — navigation with React Navigation or Expo Router, lists, forms, styling, gestures and animations with Reanimated, safe areas, accessibility and localization; networking with TanStack Query, response validation, persistence with MMKV, AsyncStorage, SQLite and secure storage; permissions, push notifications, deep links, background tasks, native modules and config plugins; performance with Hermes and the React profiler; and release with EAS Build, EAS Submit and EAS Update or bare Gradle and Xcode builds. Use when writing or changing React Native code, adding a native capability, configuring Expo, preparing a store build or an over-the-air update, or when the user asks how to do something in React Native, even if they do not name the library.

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, review, or improve data persistence using SwiftData. Use when defining @Model classes with @Attribute, @Relationship, @Transient, @Unique, or @Index; when querying with @Query, #Predicate, FetchDescriptor, or SortDescriptor; when configuring ModelContainer and ModelContext for SwiftUI or background work with @ModelActor; when planning schema migrations with VersionedSchema and SchemaMigrationPlan; when setting up CloudKit sync with ModelConfiguration; or when coexisting with or migrating from Core Data.

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Build 2D games and animations using SpriteKit. Use when creating game scenes with SKScene and SKView, adding sprites with SKSpriteNode, animating with SKAction sequences, simulating physics with SKPhysicsBody and contact detection, creating particle effects with SKEmitterNode, building tile maps, using SKCameraNode, or integrating SpriteKit scenes in SwiftUI with SpriteView.

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。

OpenClawのターミナル画面を固定のサンプルデータで試作し、実際の描画部品を使った2〜6案をtmuxで並べて操作・比較するスキル。

• 初期設定画面の案を比較したいとき
• チャットや選択画面の配置検討
• 既存画面と改善案を比べたいとき

作業の依存関係を整理し、独立した調査・実装・検証を並行して進めるためのスキルです。変更先の競合を避け、各作業の完了状況と確認結果を表にまとめます。

• 独立したファイル調査の一括実行
• 複数エージェントでの分担実装
• Git worktreeで変更先を分けたいとき

KotlinのKtorでHTTPサーバーを作るための実装例をまとめたスキル。APIのルート、認証、JSON変換、部品の組み立て、通信とテストを整理します。

• KtorでREST APIを作りたいとき
• JWT認証でルートを保護したいとき
• Koinでアプリの部品を整理したいとき

Itôのバスケットや予測市場のデータを閲覧・整理し、手元の調査メモとの比較、出典付きの市場概要、人が確認するための計画シートを作成します。

• Itôのバスケット一覧を調べたいとき
• 調査メモやウォッチリストとの比較
• 出典付きの予測市場概要の作成

Create AI avatar videos with precise control over avatars, voices, scripts, scenes, and backgrounds using HeyGen's v2 API. Use when: (1) Choosing a specific avatar and voice for a video, (2) Writing exact scripts for an avatar to speak, (3) Building multi-scene videos with different backgrounds per scene, (4) Creating transparent WebM videos for compositing, (5) Using talking photos as video presenters, (6) Integrating HeyGen avatars with Remotion, (7) Batch video generation with exact specs, (8) Brand-consistent production videos with precise control.

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。

Binance Onchain Pay enables users to buy cryptocurrency with fiat (e.g., EUR, USD) or send existing crypto from their Binance account directly to any external on-chain wallet address in a single flow—no manual withdrawal needed. Enables partners to integrate crypto buying services: - payment-method-list: Get available payment methods (Card, P2P, Google Pay, Apple Pay, etc.) with limits for a fiat/crypto pair - trading-pairs: List all supported fiat currencies and cryptocurrencies - estimated-quote: Get real-time price quote including exchange rate, fees, and estimated crypto amount - pre-order: Create a buy order and get redirect URL to Binance payment flow - order: Query order status and details (processing, completed, failed, etc.) - crypto-network: Get supported blockchain networks with withdraw fees and limits - p2p/trading-pairs: List P2P-specific trading pairs

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。

Runs the payroll-confidence chain end to end — forecasts cash across the payroll window with cash-flow-snapshot, ranks and drafts overdue-invoice reminders with invoice-chase to close any gap, then assembles timesheets, flags every anomaly, and stages the run with payroll-prep. On Gusto the run is staged for the owner to submit; on QuickBooks Payroll, or with no payroll system, the last step is a validated run sheet the owner keys in. This chain never presses the payroll button. Builds on the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), with Gmail or M365, PayPal, Square, Stripe, and either Gusto or QuickBooks Payroll adding detail, and falls back to CSV and timesheet uploads. Use it whenever payroll is the worry, including "can I make payroll," "payroll is due Friday," "plan payroll," "will I have enough to pay the crew," or "I need to run payroll but money is tight." Accepts optional horizon and payroll-date arguments.

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。

Agentic end-to-end tests with e2e, the e2e runner. Covers scaffolding e2e.config.ts, picking the Playwright browser engine or the agent-device mobile engine, starting the app under test from the config, driving flows with agent.act, judging with agent.assert, agent.waitFor, and agent.extract, pinning values with screen, app, browser, and expect, shaping the agent (context, system prompt, tools, personas), the replay cache, the e2e CLI, reading .e2e/report.json, and bug bashes (parallel explore runs proven with repro tests). Use when a project depends on e2e, when asked for end-to-end, browser, mobile, or agentic UI tests, to bug bash or hunt for bugs, or when an e2e run fails.

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 an iOS / Apple app on remote Xcode with `lim xcode build` instead of local xcodebuild, run project commands with `lim xcode run`, or run its XCTest suites with `lim xcode test`, from any environment (Linux, Windows, macOS, VM, container). Use for non-Bazel projects (an `.xcodeproj` / `.xcworkspace`, an XcodeGen `project.yml` with a gitignored project, React Native / Expo native build) when the user wants to build, compile, run code generation or Make targets, test, inspect build logs, reload, produce a preview build, or ship a signed device IPA. To run, tap, screenshot, or otherwise interact with the result on a simulator, use limrun-ios-simulator. For Bazel workspaces, use limrun-xcode-bazel.

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。

Builds security-focused full-stack web applications by implementing integrated frontend and backend components with layered security at every level. Covers the complete stack from database to UI, enforcing auth, input validation, output encoding, and parameterized queries across all layers. Use when implementing features across frontend and backend, building REST APIs with corresponding UI, connecting frontend components to backend endpoints, creating end-to-end data flows from database to UI, or implementing CRUD operations with UI forms. Distinct from frontend-only, backend-only, or API-only skills in that it simultaneously addresses all three perspectives—Frontend, Backend, and Security—within a single implementation workflow. Invoke for full-stack feature work, web app development, authenticated API routes with views, microservices, real-time features, monorepo architecture, or technology selection decisions.

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。

Draws original design and coding scenes as hand-authored SVG cards (480x360) in a two-colour brush-marker style. Identifying traits: thick wobbly black marker lines with blobby ends, built as filled ribbons; one second ink, mint green, in flat blocks that slip 2-10 px off the line like a misregistered print; solid black hair, trousers and shoes with mint highlight dashes; and a crinkly squiggle camo on cream areas, all on warm cream paper. Characters are chunky, with profile heads, big noses, dot eyes, big hands and feet. It covers people coding, thinking, carrying and resting with laptops, mugs, chairs and rubber ducks. Use when someone asks for brush, marker, felt-tip, two-colour, duotone, riso-print, zine, mint-and-black or doodle illustrations, or for empty states, onboarding, 404 pages, blog headers, feature spots or marketing cards with a bold hand-made look.

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 the effects of a close-combat energy skill as one visual language in Three.js. Lightning that reads as lightning, dynamic but never in your face: trees of strips (trunk, forks of forks, hair-fine branchlets) with a fine white core in a violet-blue glow, each channel reaching out as a stepped leader, flaring as it connects, holding its shape and fading through an afterglow, one discharge at a time; a storm-cloud orb (a lit billow surface that churns in place, 10-15 plasma-globe arcs from a small white core to its inner wall, crawlers on the rim, a hairline fresnel); ground strikes that flash when the leader arrives, crawl along the stone's own cracks and reflect in wet stone (the demo's floor is a scanned CC0 texture); a torn black shadow with dry-brush edges that hangs off the orb and streams behind it, air bursts felt as refraction, orange sparks, fractured rocks that land or float in a storm domain, and impact frames that hold, invert in two-tone ink with tapered speed lines, shake and bend the lens, limited to three flashes a second. Use for anime or dark-fantasy skill effects, lightning or thunder fists, plasma or energy balls, charge-ups, dashes with shadow trails, ground strikes, storm or domain rings, ultimates, hit-stop, impact frames, negative flashes, screen shake, "black lightning", "thunderstorm fist", "realistic lightning", "afterimage" or "skill effects only". Includes a reusable module and a five-beat demo with failure-mode switches.

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 the effects of a fire skill as one visual language in Three.js. Flames that read as fire, not as orange fog or cones: ray-marched flame volumes whose turbulence rises with buoyancy (slow at the root, accelerating and stretching as it climbs) instead of scrolling, a temperature ramp from a white-hot core a little above the fuel through yellow and orange to deep red tips, broad brighter licks inside, walls of fire broken into clumps of separate tongues of very different heights with notches between them and licks that detach and go out, a fire whirl wound in helical sheets with a waist and a blue root; charcoal smoke drawn premultiplied over the frame and lit only by the fire (cauliflower billows for a burst, a thin streaked curtain off a grass-fire front, fibrous wisps off small fires, ragged hot cracks inside a burst's smoke); embers and sparks that drift on a curl field, cool from white through orange to red and fall as ash; heat haze as refraction; firelight shared with the world; a world-space heat map that makes the ground's needles smoulder as hairlines and its pockets breathe; and gentle ink impact frames limited to three flashes a second. Use for fire or flame skills, fireballs, fire waves or breath, rings of fire, fire tornadoes or whirls, burning ground, wildfire fronts, smoke columns, embers, smouldering aftermath, "fire and smoke", "realistic fire", "flame thrower", "inferno", "burning field" or "skill effects only". Includes a reusable module and a five-beat demo on a scanned CC0 burned-ground floor with failure-mode switches.

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。

Trustpilot company profile lookup on trustpilot.com — input a company domain (e.g. apple.com, shopify.com, shopwagandtail.com) and extract company metadata: official display name, businessUnitId, TrustScore (1-5), star rating, total review count, last-12-months review count, primary category and all categories, verification flags (verifiedByGoogle, verifiedPaymentMethod, verifiedUserIdentity), claimed/closed status, website URL, contact info (email, phone, country, city, address, zip), profile image, and optional reply behavior metrics (averageDaysToReply, replyPercentage, negativeReviewsWithRepliesCount, companyUsesAIResponses). Detects company-not-found and returns isCompanyFound=false with the searched domain. Use when user mentions Trustpilot company info, Trustpilot TrustScore, look up Trustpilot rating, check Trustpilot stars, Trustpilot business profile, Trustpilot review count, Trustpilot company lookup, Trustpilot verification status, Trustpilot reply percentage, Trustpilot AI responses, business unit ID Trustpilot, get company rating from Trustpilot, scrape Trustpilot company, batch enrich domains with Trustpilot, bulk company info Trustpilot, fast domain enrichment with TrustScore, Trustpilot company metadata, Trustpilot brand monitoring, competitor TrustScore comparison, trustpilot.com domain enrichment, trustpilot company profile, trustpilot domain check, trustpilot business listing, check if company is on Trustpilot, Trustpilot category lookup, company contact info from Trustpilot. Also applies to lead-list enrichment with reputation scores, supplier vetting, agency or KOL vetting via review reputation, aggregator sites that need TrustScores for many domains, due-diligence workflows, and pre-outreach reputation checks.

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。

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

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。