433 件 ・ 関連度順
概要と使いどころ
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。
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。
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。
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。
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。
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。
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。
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。
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。
Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.
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。
Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
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 guide for writing Apache Cassandra in-JVM distributed tests (dtests). Use when creating tests that simulate multi-node Cassandra clusters within a single JVM for faster integration testing. Covers cluster creation (single-node, multi-node, multi-datacenter), configuration (all cassandra.yaml parameters, features, network topology), instance lifecycle (startup/shutdown/restart), query execution, message filtering for failure scenarios, running code on instances, ClusterUtils utilities, and debugging classloader-related issues (serialization failures, same-class-different-classloader problems).
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, run, and verify TLA+ and PlusCal formal specifications. Use for modeling distributed systems, protocols, concurrent algorithms, state machines. Can compose specs from code, find divergences between spec and implementation, spot concurrency bugs and invariant violations. Use when asked to "write a TLA+ spec", "model check", "verify protocol", "find race conditions", or "formal verification".
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 Python programming reference covering syntax, concurrency, networking, databases, ML/LLM development, and HPC. Use for: Python questions, Python interview preparation, debugging, performance optimization, async patterns, library examples, code review, best practices, MLOps workflows, distributed computing, security implementations, and any Python development tasks.
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。
比较推理副本、prefill/decode 分离、专家放置与共享 KV 的跨实例方案。用于容量规划、路由、扩缩容和故障恢复。
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。
Review Apache Pinot concurrency and state safety when locks, shared callbacks, lifecycle, or distributed metadata 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。
How to coordinate with squads on different machines using git as transport
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI 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。
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
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。