「perf」の検索結果

Expert guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).

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Safely simulates and applies Identity and Access Management (IAM) v1 (Allow) policy changes on Google Cloud. Uses the Policy Simulator to replay historical access logs against proposed policies to prevent breaking active workloads before applying the changes. Use when simulating or applying IAM v1 allow policies across Projects, Folders, or Organizations. Don't use for analyzing IAM v2 deny policies, VPC Service Controls, or performing general policy troubleshooting.

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Stores, retrieves, and manages data as objects in Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Use when you need to interact with Cloud Storage — set up a Storage MCP server (remote or local Toolbox), create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes (Standard, Nearline, Coldline, Archive), manage lifecycle and cost, protect data (versioning, CMEK, retention, Bucket Lock, holds, soft delete), host static websites, trigger Pub/Sub notifications, mount buckets (gcsfuse), or optimize performance. Covers gcloud storage / gsutil, JSON/XML APIs, client libraries, Terraform, and Cloud Storage MCP servers. Don't use for generic MCP setup that doesn't mention Cloud Storage, other MCP servers, block storage (Persistent Disk), BigQuery, or databases (Cloud SQL, Spanner, Bigtable, Firestore).

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Provides recommendations for environmental sustainability, carbon footprint reduction, and energy efficiency based on the Sustainability pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks to assess, design, or optimize Google Cloud workloads for sustainability—including the shared responsibility model, selecting low-carbon regions (CFE%), reducing resource and AI/ML energy waste, designing efficient software and storage lifecycles, or measuring and tracking emissions using Google Cloud Carbon Footprint. Don't use for financial cost reduction (use google-cloud-waf-cost-optimization), latency and throughput tuning (use google-cloud-waf-performance-optimization), or high availability and disaster recovery (use google-cloud-waf-reliability).

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 proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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 troubleshooting Apache Airflow DAGs (failed DAG runs and task instances) in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Use when figuring out reasons for DAG run or task instance failures. Don't use when looking for overall recommendations for Managed Airflow environment 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。

Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).

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Diagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).

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Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links 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。

Manages GKE storage, including PVCs, PersistentVolumes, and Filestore. Use when configuring GKE storage or creating PVCs. For GCS FUSE mounts, use google-cloud-storage-fuse. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use gke-storage-troubleshooting. Don't use for database administration or replication strategies outside volume provisioning context.

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。

Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.

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 diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).

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Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

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Apply Apache ShardingSphere's written coding standards when explicitly requested, or when code-implementation routes task-changed production, test, script, build, generated, or Maven POM artifacts through repository standards. Also perform a standalone read-only compliance audit of a user-specified scope. Use CODE_OF_CONDUCT.md, applicable AGENTS.md rules, Checkstyle, Spotless, and other repository-defined standards, including naming and evidence-based defensive-code rules. Do not invoke independently for an ordinary implementation already governed by code-implementation, and do not independently audit architecture, runtime ownership, caches, or lifecycle.

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Generate standard unit tests for one or more target classes in Apache ShardingSphere; cover requested behavior and every affected SUT-owned branch, enforce an explicitly requested numeric coverage target, and pass quality gates; perform explicit merge analysis, suitability filtering, and refactor optimization for parameterized tests.

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。

Free domain research and availability checking. No API keys or credentials required. Uses RDAP (1195+ TLDs) with whois CLI fallback for universal coverage. Checks if domains are available, searches keywords across TLDs, performs WHOIS/RDAP lookups, checks expiry dates, and finds nameservers. Use when the agent needs to: check if a domain is available, search for domains, find who owns a domain, check domain expiration, get nameservers, bulk check domains, or do any domain research. Triggers on: 'check domain', 'is domain available', 'search domains', 'domain availability', 'who owns this domain', 'whois', 'domain expiry', 'when does domain expire', 'nameservers for', 'domain research', 'find domains for', 'domain ideas', 'bulk domain check'.

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。

Performance-coaching mode for repeated failures, passive behavior, completion-quality issues, or explicit try-harder requests. Uses structured troubleshooting and evidence-first delivery habits.

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Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/stas00/ml-engineering/blob/master/skills/ml-engineering/SKILL.md

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。

macOS system performance diagnostics — see how the machine is running and what's slowing it down: CPU/GPU/memory/energy hogs, thermal throttling, memory and swap pressure, disk usage, Spotlight indexing, launchd/startup load, via a deterministic read-only CLI with known-process interpretation (kernel_task, WindowServer, mds_stores). USE WHEN mac slow, system slow, what's slowing down my mac, computer is slow, what's eating CPU, CPU usage, GPU usage, what's using the GPU, memory pressure, RAM usage, swap, runaway process, fans loud, mac running hot, thermal throttling, system health, check my mac, how's my system running, top processes, energy hogs, activity monitor, startup items, launch agents load, system taxed. NOT FOR network/wifi diagnostics, website or deployed-app health monitoring, or security scanning.

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。

Analyze one supplied URL across on-page, content, technical metadata, schema, images, and performance. Use only for a single-page review.

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。

Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", or "real CWV data".

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。

Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".

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。

Python expert for stdlib, packaging, type hints, async/await, and performance optimization

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。

React expert for hooks, state management, Server Components, and performance optimization

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。