“telemetry”的搜索结果

Wire the Prisma Next runtime — `db.ts` setup using `postgres<Contract>(...)` from `@prisma-next/postgres/runtime`, `sqlite<Contract>(...)` from `@prisma-next/sqlite/runtime`, or `mongo<Contract>(...)` from `@prisma-next/mongo/runtime`; middleware composition (telemetry from `@prisma-next/middleware-telemetry`; lints and budgets), `DATABASE_URL` config, per-environment branching, switching between Postgres, SQLite, and Mongo façades. Use for db.ts, postgres(), sqlite(), mongo(), middleware, telemetry, lints, budgets, DATABASE_URL, .env, connection pool, poolOptions, dev vs prod config, transactions, db.transaction, read replicas, multi-database, script won't exit, hangs, close connection, db.end, db.close, pool.end, [Symbol.asyncDispose], await using.

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Internal utility for the skills library itself — opt-in, local-first, privacy-respecting usage telemetry and update checks. Provides version checking, usage logging, and usage reporting for the skills in this repo. Use only when asked to view skill usage stats, configure telemetry opt-in/out, wire a skill to log its own runs, or check for skill updates.

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Instrument applications with Azure Application Insights and OpenTelemetry for end-to-end observability. Use this skill when users ask about telemetry, tracing, metrics, logging, or monitoring setup. Covers SDK setup, correlation, sampling, dashboards, and alerts per OpenTelemetry standards.

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

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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 Appli...

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

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

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

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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.

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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。

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。

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.

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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.

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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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为生产功能设计日志、指标、跨服务追踪和告警，记录请求标识与处理起点，并验证实际输出和通知，帮助了解运行状态及故障原因。

• 完善生产功能的监控信息
• 关联不同服务的日志
• 审查告警与响应操作手册

为项目复盘、故障报告等制作16:9的HTML幻灯片，采用深色CRT终端风格，以等宽文字、红色警告、时间线和审计日志组织信息。

• 汇报项目复盘结果
• 说明基础设施故障经过
• 整理安全审查结果

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.

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• 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.

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• 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.

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