「streaming」の検索結果

OpenUI generative UI with Elixir Phoenix backend. Chunked SSE streaming via Plug.Conn and Req.

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OpenUI generative UI with C# ASP.NET Core Minimal API backend. Direct OpenAI API SSE streaming via HttpClient on .NET 10.

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OpenUI generative UI with Rust Axum backend. Async SSE streaming with reqwest and async-stream.

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Use when a task needs the judgment of a Data Engineer — designing an idempotent pipeline operation so retries and backfills don't corrupt data, choosing event-time over processing-time partitioning for time-series aggregation, enforcing schema compatibility rules before an upstream change propagates downstream, adding automated data quality checks to a pipeline, or deciding between batch and streaming architecture for a specific freshness requirement.

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Master FFmpeg for video/audio conversion, compression, trimming, subtitles, streaming, and batch processing.

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Manage ML datasets with the datasets ecosystem — loading, streaming, preprocessing, versioning, and sharing. Use when data handling (not modeling) is the bottleneck.

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Build streaming AI user interfaces with AI SDK patterns — message rendering, tool-call displays, loading states, and optimistic updates. Use when creating chat UIs that stream model output and show agent activity.

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Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.

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Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.

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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

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Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.

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Android device logcat reader for real-time log monitoring. Use when working with pywayne.adb.logcat_reader module to read Android device logs via adb logcat command, supporting both C++ backend (faster) and Python backend (subprocess) with generator-style output for streaming logs.

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Blockchain USDC payments via APay — pay services, manage budgets, open streaming channels, and handle x402 protocol.

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Design asynchronous messaging patterns in Azure using Service Bus, Event Hubs, Event Grid, and Storage Queues. Use this skill when users ask for queueing, pub-sub, event streaming, retries, or decoupled system design. Covers delivery semantics, dead-lettering, idempotency, and monitoring.

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Source-grounded catalog of dynamic terminal UI/UX patterns in Claude Code 2.1.170 and Codex CLI 0.142.4 — live status lines, spinner verb sets, streaming and progress affordances. Reference for GEODE CLI/REPL UX work.

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Apache Kafka 4.2 best practices for event streaming, topic design, and producer-consumer patterns

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Filter and select sequences by criteria (length, ID, GC content, N content, motifs, patterns, description) using Biopython, streaming so large files never load into RAM. Use when subsetting a FASTA/FASTQ file, removing unwanted or low-quality records, or selecting records by specific criteria. Use the paired-end-fastq skill instead whenever the input is paired R1/R2 reads.

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Process many sequence files in batch (count, merge, split, convert, summarize) with memory-safe streaming and on-disk indexing using Biopython, pysam, or pyfastx. Use when iterating over a directory of FASTA/FASTQ files, merging or splitting datasets, building random access across many or huge files, or automating per-file operations without exhausting RAM.

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Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

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High-throughput event streaming and real-time data ingestion.

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Azure Event Hubs SDK for Rust. Use for sending and receiving events, streaming data ingestion.

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Use when building APIs on Hono (Cloudflare Workers, Bun, Deno, Node), debugging route ordering, wiring middleware, validating with @hono/zod-validator, returning streaming responses, configuring CORS with credentials, handling errors via app.onError, or composing typed RPC clients. Triggers: c.env binding types, c.header + c.redirect interaction, missing await next() bugs, Set-Cookie not attaching to redirect, route-precedence surprises, JWT middleware setup, hono/client typed RPC end-to-end. NOT for Express/Fastify/Koa idioms, tRPC/GraphQL paradigms, or Next.js Route Handlers.

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Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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Use when designing a background-job system, choosing between BullMQ / Sidekiq / RQ / Temporal / SQS, deciding queue-vs-workflow, sizing concurrency vs rate limits, building dead-letter queues, or making handlers idempotent. Triggers: jobs running twice on retry, lost jobs after worker crash, DLQ filling up, Redis OOM from job backlog, exactly-once requested, "do we need Temporal?", visibility timeout / lockDuration confusion, exponential backoff vs jitter, fan-out fan-in workflows. NOT for outbound webhook publishing (different concerns), receiver-side webhook handling (different concerns), event-streaming/Kafka topology, or in-process async (event loop only).

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