「stream」の検索結果

Expert knowledge for Azure AI Metrics Advisor development including decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alerts/hooks, or calling Metrics Advisor REST/SDKs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Stream Analytics (use azure-stream-analytics).

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 knowledge for Azure HDInsight development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with HDInsight Spark/Hive/Kafka/HBase clusters, Ambari/Oozie pipelines, or Azure-integrated storage/BI, and other Azure HDInsight related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics).

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 knowledge for Azure Data Explorer development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing ADX (RBAC/MI), automating cluster/schema deploys, tuning ingestion/queries, or integrating via ODBC/JDBC/Power BI, and other Azure Data Explorer related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics).

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 knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring IR/SSIS/SHIR, mapping data flows, SAP/SaaS connectors, CI/CD deployments, or secure VNets/Key Vault, and other Azure Data Factory related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics), Azure Databricks (use azure-databricks).

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。

Pointer to the upstream skill for React and Next.js performance work (data fetching, bundle size, re-renders), installed from upstream.

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。

Pointer to the upstream skill for building voice agents with speech-to-speech and STT-LLM-TTS pipelines, installed from upstream.

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。

Pointer to the upstream skill for writing PRDs grounded in the existing repo, with a fixed output folder, installed from upstream.

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。

Pointer to the upstream skill for behavioural guidelines that reduce common LLM coding mistakes, installed from upstream.

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。

Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing 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。

Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.

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。

Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.

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。

This skill should be used when working with Vercel AI SDK, AI Gateway, streamText, generateText, generateObject, streamObject, tool calling, or AI SDK providers. Also relevant for "ai-sdk", "@ai-sdk/*" packages, or questions about AI SDK patterns, configuration, and best practices.

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 you need to write or review programmatic JDBC with Spring — including JdbcClient (Spring Framework 7+) as the default API, JdbcTemplate only where batch/streaming APIs require JdbcOperations, NamedParameterJdbcTemplate for legacy named-param code, parameterized SQL, RowMapper mapping to records, batch operations, transactions, safe handling of generated keys, DataAccessException handling, read-only transactions, streaming large result sets, and @JdbcTest slice testing. This should trigger for requests such as Review Java code for Spring JDBC (JdbcTemplate, JdbcClient, NamedParameterJdbcTemplate); Apply best practices for Spring JDBC data access in Java code; Detect and fix SQL injection risks in JDBC code; Improve transaction boundaries or exception handling for JDBC operations; Review JdbcClient usage in Spring Framework 7. Part of Plinth Toolkit

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。

Propose a turbo skill improvement upstream by filing a GitHub issue against the turbo repo. Use when the user asks to "contribute to turbo", "submit turbo changes", "contribute back", "suggest a turbo improvement", or "upstream my 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。

Expert Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 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。

Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting 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。

Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or 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。

Routes DeepStream-Yolo deployment, model conversion, multi-GIE, and INT8 workflows for supported YOLO-family models.

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 long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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。

Fix Flutter home feed / main screen stuck on loading spinner after login when using Riverpod StreamProvider that watches GoRouter location changes. Use when: (1) Screen shows BrandedLoadingIndicator or CircularProgressIndicator permanently after successful auth redirect, (2) Widget watches a route-type-gating provider that returns AsyncValue.loading() intermittently, (3) Logs show route location oscillating between stale and current paths during post-login transition (e.g., /welcome/* after /home/0), (4) Provider chain has double-gate: widget gates on pageContext AND data provider also gates on pageContext. Distinct from riverpod-infinite-rebuild-loop (rapid rebuilds) — this causes permanent loading state, not infinite rebuilds.

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 implementing real-time AI response streaming.

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。

TanStack AI (alpha) provider-agnostic type-safe chat with streaming for OpenAI, Anthropic, Gemini, Ollama. Use for chat APIs, React/Solid frontends with useChat/ChatClient, isomorphic tools, tool approval flows, agent loops, multimodal inputs, or troubleshooting streaming and tool definitions.

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。

Search videos, analyze channel profiles, track live streams, and research content engagement on WeChat Channels (Video Accounts). Covers video search, channel profiling, live stream monitoring, comment analysis, and content collections.

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。