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Configure OpenTelemetry to send AI application traces, records of execution steps, to Confident AI. Reuse existing exporters, choose the regional endpoint, and add AI-specific attributes without requiring the deepeval Python package.
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Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.
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Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. Triggers: "AzureMonitorExporter java", "opentelemetry azure java", "application insights java otel", "azure monitor tracing java". Note: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.
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Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
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Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".
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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".
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Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit
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使用模拟环境或真实服务运行OpenClaw质量验证场景，调查并修复失败原因、检查结果报告，还可比较多个模型的表达方式和角色表现。
Investigate slow Paperclip runs, page requests, and CI suites using Honeycomb traces, which show timed operations. Find relevant traces, separate overlapping work, identify dominant delays, and configure optional tracing or add missing measurements.
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Record and query iii engine logs, traces that show how calls flow, and operational metrics. Set up reactions to incoming errors, inspect alerts, and review metrics aggregated over time windows.
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Helps implement iii workers in Node.js, browsers, Python, and Rust with language-specific guidance on installation, function registration, calls, data channels, and logging. Explains package choices and connection constraints for each environment.
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Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.
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Provides guidance for implementing OpenTelemetry instrumentation in .NET codebases, covering tracing (Activities/Spans), metrics, logs, naming conventions, error handling, performance, SDK setup, resources, context propagation, and API design best practices.
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Add OpenTelemetry tracing to MATLAB code. Use when the user asks to "add tracing", "instrument with spans", "add OpenTelemetry", "trace my code", "add observability" (when about tracing), or mentions "spans", "distributed tracing", or "OTel tracing" in the context of MATLAB functions. Covers span creation, parent-child context propagation, error handling, attributes, events, and semantic conventions.
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Expert knowledge for Azure Microsoft Opentelemetry development including configuration. Use when setting sampling, exporters, resource attributes, env vars, or tuning tracing/metrics behavior, and other Azure Microsoft Opentelemetry related development tasks. Not for Azure Monitor (use azure-monitor).
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Instrument applications with OpenTelemetry for distributed tracing, including auto and manual instrumentation, context propagation, sampling strategies, and integration with Jaeger or Tempo. Use when debugging latency issues in distributed systems, understanding request flow across microservices, correlating traces with logs and metrics for root cause analysis, measuring end-to-end latency, or migrating from legacy tracing systems to OpenTelemetry.
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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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Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.
中文简介正在准备中。当前显示原文说明。
Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.
中文简介正在准备中。当前显示原文说明。
Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Appli...
中文简介正在准备中。当前显示原文说明。
Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.
中文简介正在准备中。当前显示原文说明。
Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.
中文简介正在准备中。当前显示原文说明。
Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.
中文简介正在准备中。当前显示原文说明。
Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.
中文简介正在准备中。当前显示原文说明。