「datadog」の検索結果

Expert knowledge for Azure Partner Solutions development including troubleshooting, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when integrating Confluent/MongoDB via Service Connector, managing Datadog/Dynatrace/Elastic/New Relic, or Palo Alto Cloud NGFW, and other Azure Partner Solutions related development tasks.

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Expert at making systems observable and debuggable. Covers structured logging, metrics collection, distributed tracing, error tracking, and alerting. Knows how to find the needle in the haystack when production breaks at 3 AM. Use when "observability, logging, metrics, tracing, monitoring, error tracking, Sentry, Datadog, OpenTelemetry, debugging production, observability, logging, metrics, tracing, monitoring, sentry, prometheus, opentelemetry" mentioned.

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Analyze application error logs and monitoring platform exports (Sentry, Datadog, Rollbar) to identify error patterns, duplicates, and severity. Use when someone asks to "audit errors", "analyze exceptions", "find noisy alerts", "deduplicate error groups", or "triage production errors". Parses JSON/CSV error exports and produces categorized reports with root cause analysis.

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Restructure and optimize alert rules for monitoring platforms (Sentry, PagerDuty, Datadog, OpsGenie). Use when someone asks to "reduce alert noise", "fix alert fatigue", "create alert rules", "set up escalation policies", "tune alerting thresholds", or "create on-call runbooks". Generates platform-specific alert configurations and tiered escalation policies.

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Design error handling, structured logging, and observability with OpenTelemetry (traces, metrics, logs), error classification, recovery patterns (retry with jitter, circuit breaker, bulkhead, timeout), error budgets/SLOs with burn rate alerts, and production incident triage. Use when user asks to implement error handling, logging, monitoring, observability, OpenTelemetry, error boundaries, circuit breakers, retry logic, or SLO tracking. Do NOT use for incident runbooks (use runbook-gen), vendor-specific APM setup (Datadog, Sentry agent config), or K8s debugging.

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Create a structured on-call log by pulling alerts from monitoring and writing a summary to your team's wiki. Requires a monitoring MCP (recommended: Datadog) and a wiki MCP (Confluence or Notion).

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Expert guide for structured logging (Pino, Winston), error tracking (Sentry), log aggregation (Axiom, Datadog), request correlation, and GDPR-compliant log management / Panduan ahli untuk logging terstruktur (Pino, Winston), pelacakan error (Sentry), agregasi log, korelasi request, dan manajemen log sesuai GDPR.

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Automatic init the first time Claude acts in an incident or alerts channel — one named like #inc-…, #incident-…, #sev0-…/#sev1-…, or whose recent messages are pages or alerts from PagerDuty, Opsgenie, incident.io, Datadog, Sentry or Grafana, or relayed by people (talk about being paged is not an alert) — that isn't already a team's oncall / monitoring channel. Run once per channel, on first contact (just invited, first @-mention, channel just created), before doing what was asked: load the oncall memory oncall setup wrote to shared workspace memory, pick the owning team's section, work out which monitoring / paging / code tools Claude can reach and which are missing, post one short zero-context message, note findings in this channel's memory, then hand off to `incident-investigate` if something was asked — or immediately, unasked, when the channel was plainly opened for a live outage. Works with no oncall memory (offers setup once). Also on "load the oncall setup here" / "init this incident".

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实现日志 / 指标 / 追踪三大支柱时使用。覆盖 OpenTelemetry / Prometheus / Grafana / Jaeger / Datadog / Sentry 集成。融合 Google SRE 三支柱 + 结构化日志 + Trace Context 传播。

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Set up application observability with Prometheus, Grafana, Datadog, or CloudWatch — instrument metrics endpoints, configure Golden Signal dashboards, define alert rules with burn-rate SLOs, and add structured logging

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Use when building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.

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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs.

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[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for ...

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。