「stream」の検索結果

Builds Next.js 14+ applications with App Router, server components, server actions, middleware, streaming SSR, and Vercel deployment.

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Writes Spark jobs, debugs performance issues, configures cluster settings for distributed data processing, DataFrame transformations, and structured streaming 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。

Optimizes Active Record queries, implements Turbo Frames and Streams, configures Action Cable WebSockets, sets up Sidekiq workers, and writes RSpec test suites for Rails 7+.

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。

Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.

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。

When a solution seems impossible and the path forward is blocked. Refuses to take no for an answer: classifies what kind of "no" you hit, then runs lateral-thinking techniques (assumption autopsy, inversion, first principles, constraint toggling, analogical transfer, and more) until the wall cracks or is proven load-bearing. Generates at least 10 angles before evaluating any. AGENTS: use this on yourself when an API doesn't support what you need, an approach has failed twice, or a "that's not possible" sentence is forming — run the fast path BEFORE reporting a dead end. Archives walls to ~/.config/makerskills/unstuck/archive/. Sits upstream of decide. Triggers on "/unstuck," "I'm stuck," "this seems impossible," "we hit a wall," "there's no way to," "they said no," "dead end," "out of options," "I've tried everything," "work around this."

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。

When you want to set up an agent loop, cron-scheduled task, or recurring workflow that runs autonomously. Judgment layer on top of Claude Code's ScheduleWakeup, CronCreate, and /loop — or, on other hosts, cron/GitHub Actions plus a headless agent CLI. Picks dynamic pacing, fixed cron, or a one-shot loop; designs idempotent loop bodies; sets bail-out conditions so loops don't run forever. Examples — weekly review pulse, daily brief, hourly metric monitor, periodic vault compile, upstream-check for an adapted skill. Triggers on "/loopify," "set up a loop," "schedule this task," "run this daily," "run this weekly," "cron this," "make this recurring," "automate this on a schedule," "keep this running until X." Part of the -ify trifecta (skillify / toolify / loopify). NOT for authoring a new skill — that's skillify. NOT for adding a tool/integration — that's toolify.

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Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

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 audio — audio buses, AudioStreamPlayer, spatial audio, music management, SFX pooling, and dynamic mixing

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。

Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed. The full decision matrix for OOM (--novram / --cache-none / --disable-smart-memory), shared-VRAM creep on Windows (--reserve-vram N), model-switching with big text encoders (--cache-none), high-VRAM throughput (--gpu-only / --highvram), and attention-backend selection (--use-sage-attention for speed, --use-pytorch-cross-attention as the highest-quality / Z-Image-safe fallback). Also the acceleration-stack + Blackwell/RTX 5000 (sm_120) notes. Use when a graph OOMs (especially long video like LTX 2 / WAN), when the GPU spills into shared VRAM and slows to a crawl, when switching between models eats all RAM, when Z-Image produces black/garbled output under Sage, or when deciding which attention backend to launch with. Flag names verified against upstream comfy/cli_args.py; see Sources.

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omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existin

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This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.

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Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such

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 methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes f

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 Stack Edge development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying IoT Edge modules, GPU/Kubernetes apps, DeepStream workloads, Arc GitOps, or local ARM integrations, and other Azure Stack Edge related development tasks. Not for Azure IoT Edge (use azure-iot-edge), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Kubernetes Service Edge Essentials (use azure-aks-edge-essentials), Azure Virtual Machines (use azure-virtual-machines).

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 Speech in Foundry Tools development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using STT/TTS, custom voice or avatars, Speech containers, Voice Live, or telephony/WebRTC streaming, and other Azure Speech in Foundry Tools related development tasks. Not for Azure Content Understanding in Foundry Tools (use azure-content-understanding), Azure AI Vision (use azure-ai-vision), Azure AI Video Indexer (use azure-video-indexer), Azure Translator (use azure-translator).

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 SignalR Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring SignalR modes, upstreams, private endpoints, autoscale, multi-region topologies, or Event Grid integration, and other Azure SignalR Service related development tasks. Not for Azure Web PubSub (use azure-web-pubsub), Azure Service Bus (use azure-service-bus), Azure Event Hubs (use azure-event-hubs).

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 DocumentDB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Mongo-compatible APIs, vector search, cross-region replication, change streams, or the HTTPS Data API, and other Azure DocumentDB related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).

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 Artifacts development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, .npmrc auth, GitHub Actions CI/CD, or package retention limits, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Boards (use azure-boards).

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Pull framework updates from the upstream Aeon repo into this instance - 3-way merges canon's new commits into a PR, never clobbering operator config.

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。

Fork divergence monitor - tracks where the fleet's active forks diverge in CODE (unique commits, new/modified skills) and CONFIG (enable/var/model/schedule vs upstream), gated on real change.

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。

Evaluate and plan Amazon marketplace expansion across countries and regions. Use when a seller asks which Amazon marketplace to enter, how to compare international demand and economics, what tax, product-compliance, logistics, localization, account, or launch workstreams to investigate, or how to build a gated global-selling roadmap. Do not use as legal, tax, customs, or certification advice.

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。

Get JSON out of the model reliably. Prefer tool_use with a schema over prompted-JSON, validate on receive, retry on parse fail. Use when downstream code will parse the response.

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Applies Steve Blank's Customer Development methodology from The Four Steps to the Epiphany. Use when a startup is searching for customers and a business model before scaling. Covers Customer Discovery, Customer Validation, Customer Creation, and Company Building. Triggers include 'we built it but no one's buying', 'should we hire salespeople yet', 'how do we find our first customers', 'we're burning cash and sales aren't scaling', 'are we in a new or existing market', 'when do we scale'. NOT for companies that have already crossed the chasm into mainstream (use Crossing the Chasm instead), not for optimizing an existing sales funnel, not for product development methodology (this is its companion, not replacement).

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。

Contribute skills back to upstream via branch and PR

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。