「builds」の検索結果

Builds and debugs Shopify themes (.liquid files, theme.json, sections), develops custom Shopify apps (shopify.app.toml, OAuth, webhooks), and implements Storefront API integrations for headless storefronts. Use when building or customizing Shopify themes, creating Hydrogen or custom React storefronts, developing Shopify apps, implementing checkout UI extensions or Shopify Functions, optimizing performance, or integrating third-party services. Invoke for Liquid templating, Storefront API, app development, checkout customization, Shopify Plus features, App Bridge, Polaris, or Shopify CLI 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。

Builds iOS/macOS/watchOS/tvOS applications, implements SwiftUI views and state management, designs protocol-oriented architectures, handles async/await concurrency, implements actors for thread safety, and debugs Swift-specific issues. Use when building iOS/macOS applications with Swift 5.9+, SwiftUI, or async/await concurrency. Invoke for protocol-oriented programming, SwiftUI state management, actors, server-side Swift, UIKit integration, Combine, or Vapor.

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。

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

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。

Writes, reviews, and debugs idiomatic Rust code with memory safety and zero-cost abstractions. Implements ownership patterns, manages lifetimes, designs trait hierarchies, builds async applications with tokio, and structures error handling with Result/Option. Use when building Rust applications, solving ownership or borrowing issues, designing trait-based APIs, implementing async/await concurrency, creating FFI bindings, or optimizing for performance and memory safety. Invoke for Rust, Cargo, ownership, borrowing, lifetimes, async Rust, tokio, zero-cost abstractions, memory safety, systems programming.

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 Django web applications or REST APIs with Django REST Framework. Invoke when working with settings.py, models.py, manage.py, or any Django project file. Creates Django models with proper indexes, optimizes ORM queries using select_related/prefetch_related, builds DRF serializers and viewsets, and configures JWT authentication. Trigger terms: Django, DRF, Django REST Framework, Django ORM, Django model, serializer, viewset, Python web.

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 concurrent Go patterns using goroutines and channels, designs and builds microservices with gRPC or REST, optimizes Go application performance with pprof, and enforces idiomatic Go with generics, interfaces, and robust error handling. Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration, CLI tools, benchmarks, or table-driven testing.

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。

Writes and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and CI/CD 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。

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。

Builds, optimizes, and debugs cross-platform mobile applications with React Native and Expo. Implements navigation hierarchies (tabs, stacks, drawers), configures native modules, optimizes FlatList rendering with memo and useCallback, and handles platform-specific code for iOS and Android. Use when building a React Native or Expo mobile app, setting up navigation, integrating native modules, improving scroll performance, handling SafeArea or keyboard input, or configuring Expo SDK 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。

Use when building C# applications with .NET 8+, ASP.NET Core APIs, or Blazor web apps. Builds REST APIs using minimal or controller-based routing, configures database access with Entity Framework Core, implements async patterns and cancellation, structures applications with CQRS via MediatR, and scaffolds Blazor components with state management. Invoke for C#, .NET, ASP.NET Core, Blazor, Entity Framework, EF Core, Minimal API, MAUI, SignalR.

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 demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in 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。

Cold-start interview — builds your watchlist, indexes the policy library, and learns your materiality threshold so the monitor surfaces signal instead of noise. Use on fresh install, when reconfiguring (--redo), or when re-checking what connectors are actually responding (--check-integrations).

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。

Cold-start setup — learns your jurisdictional footprint and escalation rules from your handbook and termination memos. Asks which states and countries have employees, reads seed documents, and builds a jurisdiction-aware escalation table. Use on fresh install, when CLAUDE.md still has [PLACEHOLDER] markers, or when re-running with --redo or --check-integrations.

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。

What's blocking close — maintain the closing checklist with status, critical path, and days to close. Self-updating: ingests new items from diligence findings and schedule builds, tracks status, surfaces what's blocking. Use when user says "closing checklist", "what's left to close", "checklist status", "add to the checklist", or on a scheduled status pull.

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。

Analyzes Axiom query patterns to find unused data, then builds dashboards and monitors for cost optimization. Use when asked to reduce Axiom costs, find unused columns or field values, identify data waste, or track ingest spend.

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 reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary 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。

Designs and builds Axiom dashboards via API. Covers chart types, APL and metrics/MPL query patterns, SmartFilters, layout, and configuration options. Use when creating dashboards, migrating from Splunk, or configuring chart options.

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。

Turn an idea or objective into a goal package for /goal. Interviews the user, builds a reviewed fact sheet via Plannotator, then explores the codebase to produce an execution 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。

Split one oversized commit or branch into a stack of independently reviewable Graphite (gt) PRs - deciding what genuinely separates, what is atomic and must stay whole, and proving each lower PR builds and passes without the ones above it. Use when asked to "split this PR", "this commit is too big", "break this into a stack", "can we separate this out", or when a diff bundles several concerns across different layers.

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 UI from a Figma file or a design image so it matches the design, using your project's existing tokens 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。

Builds multiple variants of a component you're working on and helps you iterate and pick one.

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 and applies fuzzing dictionaries so a fuzzer can produce the keywords, magic bytes, and tokens a target expects. Covers extracting tokens from source, headers, binaries, and specifications, dictionary syntax, and wiring one into libFuzzer or AFL++. Use when fuzzing a parser, protocol, or file format, when coverage stalls at input validation, or when a target compares against fixed strings.

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。

Sets up and runs Ruzzy, Trail of Bits' coverage-guided Ruby fuzzer and the only production-ready one for the language. Covers harness structure, fuzzing pure Ruby and the native C extensions in gems, and sanitizer builds. Use when fuzzing a Ruby library or gem, testing a Ruby C extension for memory safety, or asking how to fuzz Ruby at all.

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 custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or architecture, implementing a fuzzing research idea, or when libFuzzer and AFL++ lack the control you need.

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。