'telemetry' 검색 결과

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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...

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Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reads a specific actor's live telemetry from a running CARLA server — location and rotation, velocity and speed, acceleration, angular velocity, and for vehicles the control input (throttle/steer/brake), front-wheel steer angle and mass. All kinematics come from one world snapshot so they are frame-consistent. Use when the user asks "how fast is it going", "where/what heading is the car", "show the ego's telemetry", or "stream a vehicle's state". Resolve ambiguous targets with the world-data skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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. NOT for OTel metrics/logs pipelines, vendor backend setup (Datadog/Honeycomb/Jaeger/Tempo), or browser RUM tracing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guidelines for fixing unhandled errors from the VS Code error telemetry dashboard. Use when investigating error-telemetry issues with stack traces, error messages, and hit/user counts. Covers tracing data flow through call stacks, identifying producers of invalid data vs. consumers that crash, enriching error messages for telemetry diagnosis, and avoiding common anti-patterns like silently swallowing errors.

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Add logs, metrics, traces that follow requests across services, and actionable alerts to production features. This skill guides signal design, request identification, response instructions, and checks that the collected data can explain failures.

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• Add monitoring signals to a new production feature
• Connect logs across services and background jobs
• Review alerts and their response instructions

Create 16:9 HTML presentations for project debriefs, security reviews, and incident reports. A dark CRT terminal style combines monospaced text, red alerts, data grids, timelines, and audit logs to present dense operational information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Presenting project debriefs
• Reporting infrastructure incidents
• Organizing security review findings

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow requests across services
• Understanding service dependencies
• Tracing how errors spread

Use Stratus Red Team to simulate attack techniques across AWS, Azure, GCP, and Kubernetes. Prepare test resources, run and repeat simulations, check logs and alerts, and remove resources to assess whether security detections work.

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• Checking whether cloud detection rules trigger
• Testing log ingestion and alert routing
• Running joint attack and defense exercises

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Audit Python SDK logs, exceptions, and telemetry for sensitive data exposure. Trace values to their sources, fix confirmed leaks, and test that redaction hides sensitive information while preserving the caller's expected behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit logs and exceptions for sensitive data exposure
• Fix leaks involving model or tool data
• Test redaction and caller behavior

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Assess LoRaWAN and sub-GHz wireless devices by capturing signals, examining packet formats, and checking key reuse and replay weaknesses, where recorded messages are resent. It covers IoT networks, fixed-code remotes, and proprietary radio protocols.

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• Reviewing LoRaWAN keys and frame counters
• Testing fixed-code remotes for replay weaknesses
• Analyzing proprietary wireless packet formats