'prometheus' 검색 결과

Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.

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• Setting up GKE logging and metrics collection
• Investigating missing Prometheus metrics
• Planning dashboards and alerts for cluster health

Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.

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• Refreshing cost metrics for Grafana or Prometheus
• Sending a one-time cost report to a webhook
• Providing budget utilization data for monitoring

Configure Prometheus to collect and store operational metrics from infrastructure and applications. Covers collection schedules, alert rules, automatic discovery of monitored services, and troubleshooting checks to help establish and maintain monitoring.

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• Setting up infrastructure and application monitoring
• Configuring metric collection intervals and retention
• Designing alert rules

Query Prometheus metrics through Grafana and manage dashboards as JSON files. Use it to investigate current values and trends, add or adjust panels, and preview dashboard updates before applying them.

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• Investigating error rates and response times
• Adding panels to a Grafana dashboard
• Managing dashboard configuration as JSON

Design service monitoring with Prometheus, write PromQL queries to analyze metrics, and build Grafana dashboards. Configure actionable alerts and manage metric labels so monitoring stays useful as the volume of collected data grows.

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• Monitor service latency, traffic, and errors
• Review PromQL queries and precomputed metrics
• Group related alerts and filter brief spikes

Define service reliability measurements and targets, then turn them into Prometheus monitoring rules and alerts. Calculate error budgets, the amount of failure allowed, and outline Grafana dashboards for tracking reliability and deciding when to prioritize fixes.

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• Setting availability and response time targets
• Calculating and tracking error budgets
• Creating alerts for rapid error budget consumption

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

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• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.

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• Adding searchable JSON logs
• Collecting service metrics with Prometheus
• Following requests across multiple services

Prometheus provider for obz. Covers all 6 metric commands using standard PromQL. This skill should be used when the user mentions "Prometheus", "PromQL", "obz metric -p prom", or needs to query metrics from a Prometheus server.

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Design production alerts, route notifications by severity, and organize on-call schedules. Use configuration examples to define backup contacts, escalation steps when alerts go unanswered, and runbooks that guide responders through incident investigation.

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• Define alert thresholds and severity levels
• Route notifications to Slack and PagerDuty
• Plan on-call rotations and holiday coverage

Build Grafana dashboards that show application and infrastructure health through request rates, errors, response times, and resource usage. Organize metrics into useful panels, add filters and alerts, and manage dashboard definitions as code.

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• Build an API health dashboard
• Visualize CPU and memory usage
• Track service level objectives and business KPIs

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

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• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Define reliability targets for production services and calculate error budgets, the allowed amount of failure. Create monitoring rules, incident response runbooks, capacity plans, and automation scripts, with failure experiments to check recovery behavior.

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• Define service reliability targets and error budget policies
• Design monitoring dashboards and actionable alerts
• Prepare incident response runbooks and postmortems

Plan, implement, or review prometheus configuration work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests prometheus configuration work.

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Site reliability specialist for Prometheus metrics, distributed tracing, alerting strategies, and SLO designUse when "observability, monitoring, prometheus, grafana, alerting, slo, sli, metrics, tracing, logging, on-call, incident, observability, prometheus, grafana, tracing, jaeger, alerting, slo, sli, metrics, logging, sre, ml-memory" mentioned.

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Query and manage Grafana dashboards and Prometheus metrics for Happy infrastructure. Covers grafanactl CLI usage, direct Prometheus queries through Grafana proxy, and dashboard-as-code workflows. Use when user asks about metrics, dashboards, monitoring, Grafana, Prometheus, or wants to add/modify panels.

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Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.

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インフラストラクチャとアプリケーションの包括的なメトリクス収集、ストレージ、モニタリングのためのPrometheusをセットアップします。メトリクス収集の実装、モニタリングインフラストラクチャのセットアップ、アラートシステムの設定時に使用します。

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Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.

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• Plan fallback procedures for model provider outages
• Investigate declining answer quality after a deployment
• Prepare emergency controls for token spending spikes

Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.

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• Investigate slow agent responses
• Track token usage and estimated costs
• Trace failures in model and tool calls

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

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• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Turn existing PromQL or ListTimeSeries queries into Cloud Monitoring chart definitions in textproto, a text format for Protocol Buffers. Generate readable titles, axis labels, units, and plot types, then validate the resulting widget files.

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• Create chart definitions from existing monitoring queries
• Set chart titles, axis labels, and units
• Prepare widget definitions for dashboard provisioning

Install and configure WizTelemetry Platform Service, the shared API backend for KubeSphere observability extensions. Generate configuration based on installed extensions, update installation settings, and inspect service health, pods, and logs.

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• Install the shared backend for observability extensions
• Regenerate configuration after changing extensions
• Check API server and console health

Use this skill to deploy and operate a CollectX (clx) based DOCA telemetry collector on a host or BlueField — wiring providers / counters into the collector, running the collection daemon, and shaping its exporters (Prometheus pull, Fluent Bit push, NetFlow, file / IPC) so the metrics actually leave the box. Trigger even when the user never says CollectX or clx — implicit phrasings: {collector emits nothing downstream}, {add a provider to the clx collector}, {turn on the Prometheus endpoint}, {ship counters to Fluent Bit from the DPU}, {daemon starts but no schema rows appear}. This skill owns the CollectX collection mechanism plus the operator's own doca-telemetry / doca-telemetry-exporter usage; it ROUTES the productized DOCA Telemetry Service (DTS) to public docs (AGENTS.md Non-goal #7), the reader API to doca-telemetry, and the publisher API to doca-telemetry-exporter. Refuse to invent clx symbols, provider names, schema fields, flags, or config paths — describe the class and route to the live source.

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