3,039 件 ・ 関連度順
概要と使いどころ
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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 a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
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。
Cut a new semver release — bump all version strings via bump-version.ts, open a release PR, and after merge tag main and push. Use when cutting a release, tagging a version, shipping a build, or preparing a deployment. Trigger keywords - cut tag, release tag, new tag, cut release, tag version, ship it.
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。
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
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。
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
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。
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
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。
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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。
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.
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Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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 and maintain OpenViking server - configure, install, start, stop, and cleanup the server. Use when need to setup or manage OpenViking service deployment.
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。
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
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。
Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management
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。
Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges
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。
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
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 creating new skills, editing existing skills, or verifying skills work before deployment
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 and test Polymarket prediction market trading strategies for YES/NO token trading. Provides 6 tools: get_all_prediction_events (browse markets, $0.001), get_prediction_market_data (analyze price history, $0.001), create_prediction_market_strategy (generate code, $1-$4.50), run_prediction_market_backtest (test performance, $0.001). Trade on real-world events (politics, economics, sports, crypto). Currently simulation only (live deployment coming soon).
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 and ship software — code, implement, develop, API, backend, frontend, deploy, infrastructure, CI/CD, databases, cloud, mobile, AI/ML. The CTO office's broadest execution domain, applied with TDD discipline.
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Publish presentations and web content to GitHub Pages. Converts PPTX, PDF, HTML, or Google Slides to a live GitHub Pages URL. Handles repo creation, file conversion, Pages enablement, and returns the live URL. Use when the user wants to publish, deploy, or share a presentation or HTML file via GitHub Pages.
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。
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
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Build and scale partner ecosystems that drive revenue and platform adoption. Use when building partner programs from scratch, tiering partnerships, managing co-marketing, making build-vs-partner decisions, or structuring crawl-walk-run partner deployment.
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。
Generate comprehensive rollout plans with preflight checks, step-by-step deployment, verification signals, rollback procedures, and communication plans for infrastructure and application 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。
Comprehensive skill for working with Azure DevOps REST API across all services including Boards (work items, queries, backlogs), Repos (Git, pull requests, commits), Pipelines (builds, releases, deployments), Test Plans, Artifacts, organizations, projects, security, extensions, and more. Use when implementing Azure DevOps integrations, automating DevOps workflows, or building applications that interact with Azure DevOps services.
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。
CI/CD pipeline patterns with GitHub Actions, release automation, and testing strategies. Use for: github actions, workflow, CI, CD, pipeline, deploy, release, semantic release, changesets, goreleaser, matrix, cache, secrets, environment, artifact, reusable workflow, composite action.
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。
Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.
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。