「monitoring」の検索結果

Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).

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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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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

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Daily brand-mention monitoring loop for {{brand_terms}}. Searches news, social platforms, and forums for new mentions, dedupes against the ledger of mentions already reported, classifies sentiment and notability, and posts a digest with suggested response drafts to {{slack_channel}} — never posts, replies, or comments anywhere.

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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.

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Scrapes social platforms, business data, and e-commerce via Apify actors — Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps, Amazon, and web crawls — filtering in code. USE WHEN scrape Instagram, scrape LinkedIn, scrape TikTok, scrape YouTube, scrape Facebook, Google Maps leads, Amazon reviews, business intelligence, multi-platform social listening, competitive analysis, lead generation, social monitoring, Apify actors, web crawl, extract contacts. NOT FOR X/Twitter account operations like posting, threads, or bookmarks (those need a dedicated X API client), 4-tier progressive scraping with proxy escalation (use BrightData), or real-Chrome bot bypass and computer use (use Interceptor).

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SEO drift monitoring: capture baselines of SEO-critical elements, detect changes, and track regressions over time. Git for SEO: baseline, diff, and track changes to your on-page SEO. Use when user says "SEO drift", "baseline", "track changes", "did anything break", "SEO regression", "compare SEO", "before and after", "monitor SEO changes", or "deployment check".

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System administration expert for Linux, macOS, Windows, services, and monitoring

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Prometheus monitoring expert for PromQL, alerting rules, Grafana dashboards, and observability

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This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.

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Search Zhihu for content using the search_v3 API. Use when user mentions Zhihu, 知乎, searching Zhihu, searching 知乎, Zhihu search, 知乎搜索, searching for answers/articles on Zhihu, or says 搜一下知乎, 在知乎上搜索, 找知乎上的, 知乎上搜搜. Also applies to exploring Zhihu's internal API endpoints, extracting search results in bulk, and monitoring Zhihu for new content on specific topics.

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Use when creating, running, or managing CI/CD pipelines in KubeSphere DevOps, including pipeline API operations and run monitoring

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Automate Cloudinary media management including folder organization, upload presets, asset lookup, transformations, and usage monitoring through natural language commands

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Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.

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Use when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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Comprehensive backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware, or working with Express APIs, Prisma database access, Sentry error tracking, Zod validation, unifiedConfig, dependency injection, or async patterns. Covers layered architecture (routes → controllers → services → repositories), BaseController pattern, error handling, performance monitoring, testing strategies, and migration from legacy patterns.

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Add Sentry v8 error tracking and performance monitoring to your services. Use this skill when adding error handling, creating new controllers, instrumenting cron jobs, or tracking database performance. ALL ERRORS MUST BE CAPTURED TO SENTRY - no exceptions.

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Operate professional paid advertising across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for account intake, source-grounded audits, strategy, budget and measurement planning, creative production, experiments, reporting, monitoring, and explicitly approved campaign changes. Also trigger on PPC, paid social, retail media, attribution, tracking, landing pages, cross-platform conversion totals, negative keywords or search terms, beta-feature scoring, stale platform claims, API-token or credential setup, campaign deletion, and safe Claude Ads installation or uninstall.

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Monitor paid-ad account pacing, delivery, performance, creative fatigue, tracking, policy, and data quality across supported platforms. Use for daily or weekly checks, anomaly review, budget pacing, post-launch verification, or campaign monitoring.

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Domain specification for a personal daily monitoring dashboard. Defines all target panels (email, Feishu, Office, social, news, Twitter, code status, schedule, stock market, daily finance), their data sources, APIs, and priority ordering for incremental development.

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Comprehensive Linux privilege escalation methodology for offensive security engagements. Covers the full attack surface from a low-privilege shell to root: SUID/SGID binary abuse via GTFOBins, Linux capabilities exploitation (cap_setuid, cap_dac_override, cap_dac_read_search), sudo misconfigurations including NOPASSWD rules and Baron Samedit (CVE-2021-3156), cron job abuse through writable scripts, PATH hijacking, and wildcard injection with tar/rsync/chown. Includes writable /etc/passwd attacks, NFS no_root_squash exploitation, kernel exploits (DirtyPipe CVE-2022-0847, DirtyCow CVE-2016-5195, PwnKit CVE-2021-4034), Docker group container escapes, LD_PRELOAD and LD_LIBRARY_PATH hijacking for shared library injection, systemd service misconfigurations, and sensitive file enumeration for credential harvesting. Integrates automated enumeration with LinPEAS, linux-exploit-suggester, pspy for process monitoring, and GTFOBins for binary exploitation. Each technique includes detection signatures and defender-side visibility to support purple team operations. Maps to MITRE ATT&CK T1548 (Abuse Elevation Control Mechanism) and related sub-techniques. Designed for authorized penetration testing, red team engagements, and CTF competitions where you hold a low-privilege shell and need to escalate to root.

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。