495 件 ・ 関連度順
概要と使いどころ
Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and 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。
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
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。
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
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。
Tie an LP statement to the fund's NAV pack — recompute the LP's capital account from the NAV components and flag any line that doesn't agree. Use before LP statements are distributed.
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 the user wants to "package an MCP server", "bundle an MCP", "make an MCPB", "ship a local MCP server", "distribute a local MCP", discusses ".mcpb files", mentions bundling a Node or Python runtime with their MCP server, or needs an MCP server that interacts with the local filesystem, desktop apps, or OS and must be installable without the user having Node/Python set up.
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。
Replay-first debug flow for SGLang serving problems. Use when a live or recent server shows health-check failures, latency or throughput regressions, queue growth, timeouts, distributed stalls, crash dumps, wrong outputs after deploys, or PD/EP/HiCache issues, and the job is to turn the problem into a replay plus the right next debug tool.
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。
Systematically collects, categorizes, and distributes indicators of compromise (IOCs) during and after security incidents to enable detection, blocking, and threat intelligence sharing. Covers network, host, email, and behavioral indicators using STIX/TAXII formats and threat intelligence platforms. Activates for requests involving IOC collection, indicator extraction, threat indicator sharing, compromise indicators, STIX export, or IOC enrichment.
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 an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe analyst sharing or automating threat intel distribution to TAXII/MISP feeds.
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。
Deploy MISP via Docker and configure feeds from sources like abuse.ch, AlienVault OTX, and CIRCL to aggregate, correlate, and distribute threat intelligence, including automated feed synchronization and STIX/TAXII-based integration with Splunk, Elasticsearch, and SOAR platforms. Use when standing up centralized IOC management or wiring multi-source threat feeds into a SIEM.
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 backend architect specializing in scalable API design, microservices architecture, and distributed systems.
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 guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
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。
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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。
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.
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 guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
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。
Create, organize, and distribute content across Workspace.
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。
Closes the books and turns them into a decision as a three-link chain — month-end-prep reconciles the ledger against every connected payment processor and writes the P&L narrative, cash-flow-snapshot then refreshes the 30/60/90-day forecast off the newly closed numbers rather than raw ones, and report-builder publishes and distributes the close packet. Requires a ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) and uses Gusto, PayPal, Ramp, Shopify, Square, and Stripe when connected, falling back to statement or CSV uploads. Trigger on "close the month," "month-end," "close the books," "reconcile," "send the close packet to my accountant," or when the owner asks why revenue or margin moved last month. An owner who wants only the reconciliation and its flags, with no forecast refresh and no packet distribution, routes to month-end-prep directly.
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。
Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services. Use when investigating slow requests, analyzing trace hierarchies, or resolving latency regressions. Do NOT use for querying non-GCP telemetry or database query optimization outside Cloud Trace.
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。
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).
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。
Manage GPU compute jobs on the Qizhi (启智) platform using qzcli — a kubectl-style CLI tool. Use when user says "qzcli", "启智平台", "submit job", "stop job", "查计算组", "avail", "list jobs", "batch submit", or needs to manage distributed training jobs on a Qizhi 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。
Use when installing or configuring the OpenSearch extension for KubeSphere, which provides distributed search and analytics engine for storing logs, events, auditing, and notification history
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。
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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。
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.
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 guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
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 writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build 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。