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Found 10 Skills
Create, track, retrieve, update, and delete custom business metrics for AI Configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.
Create, track, retrieve, update, and delete custom business metrics for configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.
Emit and query Vercel Custom Metrics. Use when instrumenting application or business measurements in Vercel Functions, using metric() from @vercel/functions, choosing metric names and attributes, or querying emitted values with vc metrics.
OpenTelemetry observability for .NET 10 applications. Covers traces, metrics, and logs using the OpenTelemetry SDK with OTLP export. Includes custom ActivitySource, IMeterFactory metrics, resource configuration, and Aspire Dashboard integration. Load this skill when setting up distributed tracing, custom metrics, OTLP export, or when the user mentions "OpenTelemetry", "OTLP", "traces", "spans", "Activity", "ActivitySource", "metrics", "IMeterFactory", "Meter", "Counter", "Histogram", "Gauge", "telemetry", "observability", "distributed tracing", "OTEL", or "Aspire Dashboard".
Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.
Build and run LLM-as-judge evaluation pipelines using Amazon Bedrock Evaluation Jobs with pre-computed inference datasets. Use when setting up automated model evaluation, designing test scenarios, collecting pre-computed responses, configuring custom metrics, creating AWS infrastructure, running evaluation jobs, parsing results, and iterating on findings.
System metrics, telemetry, and performance monitoring
Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
Configure autoscaling for Kubernetes, VMs, and serverless workloads based on metrics, schedules, and custom indicators.
Setup Sentry Metrics in any project. Use when asked to add custom metrics, track counters/gauges/distributions, or instrument application performance. Supports JavaScript and Python.