1,328 件 ・ 関連度順
概要と使いどころ
Use when upgrading an existing TypeScript codebase from Inngest SDK v3 to v4, or when fixing mixed v3/v4 API usage. Covers detecting current SDK usage, moving triggers into createFunction options, replacing EventSchemas with eventType/staticSchema, moving serve options to the client, updating realtime imports, rewriting step.invoke string IDs, checkpointing/serverless runtime settings, Connect option changes, and verification.
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。
Verify Next.js runtime behavior after editing app code. Use this skill to confirm a change actually works in a running app — not just that it compiles or type-checks. Combines /_next/mcp (Next.js's view) with agent-browser (the browser's view). Requires a running `next dev`.
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 animations for Expo and React Native only, using Reanimated, Gesture Handler, Expo Router, and expo-haptics. Use when the target runtime is an Expo or React Native app. Do not use it for Core Next.js web motion; use `animate` for web. Only runs when explicitly invoked; it does not trigger on its own.
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。
Provides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Creates Lambda handler classes, configures runtime layers, sets up SQS/SNS event triggers, implements warm-up strategies, and optimizes cold starts. Use when deploying PHP/Symfony applications to AWS Lambda, configuring API Gateway integration, implementing serverless PHP applications, or optimizing Lambda performance with Bref. Triggers include "create lambda php", "deploy symfony lambda", "bref lambda aws", "php lambda cold start", "aws lambda php performance", "symfony serverless", "php serverless framework".
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 reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.
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。
Test a consequential technical assumption with a small, falsifiable experiment before committing to an approach. Use when a plan depends on uncertain runtime, integration, or data behavior that inspection alone cannot establish. Not for preference interviews or routine implementation.
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。
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
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, modify, debug, and cloud-validate VeADK Agents from an accepted product intent. Use for VeADK architecture, tools, workflows, memory, knowledge, executable AgentKit projects, local tests, and AgentKit build/deploy/invoke/log repair loops. Do not use for unrelated user requests, other Agent frameworks, standalone cloud administration, or production Runtime operations.
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。
Uses Managed Agents' 14.5-hour runtime to audit an entire codebase overnight. Security, performance, accessibility, dependency issues. You wake up to a full report.
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。
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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。
Provisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality evaluation.
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。
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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 XcodeBuildMCP to build, run, launch, and debug the current iOS project on a booted simulator. Trigger when asked to run an iOS app, interact with the simulator UI, inspect on-screen state, capture logs/console output, or diagnose runtime behavior using XcodeBuildMCP tools.
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。
Operate FATE 2.2.0 federated-learning deployments, FateFlow Pipeline workflows, local launchers, and component CLI/runtime inspection.
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。
Operate the legacy Frustum PointNets code release for KITTI or SUN RGB-D data preparation, TensorFlow runtime setup, training, inference, and 3D detection evaluation.
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 this repo skill for DiscoArt image generation, configuration/prompt scheduling, CLI, Jina serving, Docker runtime planning, and troubleshooting.
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 the official Elasticsearch Python client for cluster connections, generated APIs, async code, bulk ingestion, query DSL, ES|QL, optional integrations, and runtime troubleshooting.
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 DB-GPT 0.8.1 as an agentic AI data platform: install and configure provider profiles, operate its CLI and services, build agents and AWEL flows, ingest and retrieve data with RAG, call client/API surfaces, and execute code through explicitly bounded sandbox runtimes.
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 deep-daze for CLIP-guided Siren image generation through the imagine CLI and Python APIs, with runtime inspection and troubleshooting.
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 richzhang/colorization PyTorch colorizers for automatic image colorization, Python API calls, preprocessing/postprocessing, and runtime troubleshooting.
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 Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone 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 BentoML to author model-serving Services, build and containerize Bentos, run HTTP/gRPC servers and clients, manage models, operate the CLI and BentoCloud, and configure observability or production runtime behavior. Use when tasks mention BentoML, bentoml service.py, bentofile.yaml, Bento build/serve/deploy, BentoCloud, or BentoML model stores.
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 this skill for maintaining, migrating, debugging, and safely operating Microsoft AutoGen Python 0.7.x applications, including AgentChat, Core runtimes, extensions, Studio, Magentic-One, AG Bench, and pyautogen compatibility.
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。
Routes automl_gs tabular AutoML searches and the generated runtime artifacts that those searches produce.
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。