510건 · 관련도순
Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
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Use when answering Scenario admin analytics questions about Creative Unit consumption, most-used models, usage by user or project, AI adoption observability, cached usage data, CSV exports, enterprise PDF reports with charts, or a local analytics dashboard. Keywords: usage API, CU, analytics, adoption, reporting, consumption, cache.
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Design and run a monitoring system for a website or web app. Use this skill when setting up uptime checks, defining SLOs, configuring error tracking, choosing what to alert on, designing on-call rotations, or fixing alert fatigue. Triggers on monitoring, alerts, uptime, SLO, SLA, error rate, on-call, pager, alert fatigue, observability, dashboards, what should we monitor. Also triggers when an incident reveals a gap in monitoring.
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Cilium and Hubble network observability for Kubernetes. Use when managing network policies, observing traffic flows, or troubleshooting connectivity with eBPF-based networking.
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Manage Istio service mesh for traffic management, security, and observability. Use for traffic shifting, canary releases, mTLS, and service mesh troubleshooting.
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Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
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Your software-engineering brain trust. Named personas across 11 cells covering cloud architecture, reliability/SRE/observability, data and storage, security, FinOps, languages and runtimes, systems programming, web and frontend…
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Expert knowledge for Azure Spring Apps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring ASA networking/security, Tanzu tools, observability/APM, CI/CD deployments, or blue‑green releases, and other Azure Spring Apps related development tasks. Not for Azure App Service (use azure-app-service), Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Functions (use azure-functions).
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Fleet skill observability with two views - health audits per-skill metrics and files/resolves issues in memory/issues/; analytics ranks the fleet by 7d runs, success rates, and anomaly flags.
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Monitoring, observability, operational procedures, CI/CD lessons learned, and quality gate definitions. Load when assessing production readiness or validating operational excellence.
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Platform design review critique dimensions and severity levels. Load when reviewing CI/CD pipelines, infrastructure, deployment strategies, observability, or security designs.
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Designs CI/CD pipelines, infrastructure, observability, and deployment strategy. Use when preparing platform readiness for a feature.
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Implements structured diagnostics, correlation and justified metrics or traces; not incident investigation or a mandatory telemetry platform.
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Configure structured logging with Pino, Prometheus metrics, and health checks for NestJS services. Use when adding JSON logging, request tracing with correlation IDs, Prometheus metric endpoints, or liveness/readiness health checks.
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Standards for structured logging and observability in Golang. Use when adding structured logging or tracing to Go services.
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Enforce structured JSON logging, OpenTelemetry distributed tracing, and RED metrics across backend services. Use when adding request correlation, setting up tracing spans, defining SLO burn-rate alerts, or instrumenting middleware.
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Instrument Spring Boot with Micrometer metrics, distributed tracing, and structured logging. Use when adding Micrometer metrics, distributed tracing, or structured logging to Spring Boot.
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Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.
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Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
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Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.
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Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
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检测并治理可观测资产漂移、增量更新与质量问题，输出资产登记与治理报告。
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Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.
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Design an industrial recommendation system or subsystem with explicit scale, latency, freshness, consistency, availability, privacy, cost, degradation, observability, and rollout constraints.
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