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Agent Skill · 开发

opentelemetry(OpenTelemetry 命令行)

opentelemetry 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

opentelemetry 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 16,500。

总安装 494周安装 21GitHub Stars 315下载 173核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:opentelemetry(OpenTelemetry 命令行)
来源仓库:https://github.com/codewithmukesh/dotnet-claude-kit
仓库路径:skills/opentelemetry
安装命令:
npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill opentelemetry
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill opentelemetry

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于分布式追踪、指标与日志三位一体观测体系搭建。适用于单 AddOpenTelemetry() 调用完成三支柱配置。可协助设置 OTLP exporter 与环境变量控制路由。强调 IMeterFactory 注入与活动标签安全处理。使用前请确认后端收集器地址与服务命名规范。

opentelemetry 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/opentelemetry

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill opent…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

opentelemetry 怎么安装?

在终端执行 npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill opentelemetry。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

opentelemetry 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

opentelemetry 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

opentelemetry 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

opentelemetry 用的人多吗?

公开数据是累计安装 494 次,近一周安装 21 次,下载 173 次,GitHub 315 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

opentelemetry 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
opentelemetry

OpenTelemetry 命令行 · 本页

494315接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

OpenTelemetry

Core Principles

  1. Three pillars, one setup — Configure traces, metrics, and logs through a single AddOpenTelemetry() call. Use UseOtlpExporter() for cross-cutting export to any OTLP-compatible backend.
  2. Use IMeterFactory for metrics — Never create Meter instances with new. The factory manages lifetime through DI and prevents leaks.
  3. Null-safe activities — StartActivity() returns null when no listener is attached. Always use ?. when setting tags or events.
  4. Environment variables over code — Use OTEL_EXPORTER_OTLP_ENDPOINT and OTEL_SERVICE_NAME so deployments control telemetry routing without code changes.
  5. Low-cardinality metric tags — Keep metric tag combinations under ~1000 per instrument. Use span attributes or logs for high-cardinality data like user IDs or request IDs.

Patterns

Full Setup with All Three Signals

// Program.cs
var builder = WebApplication.CreateBuilder(args);

builder.Services.AddOpenTelemetry()
    .ConfigureResource(resource => resource
        .AddService(
            serviceName: builder.Environment.ApplicationName,
            serviceVersion: "1.0.0"))
    .WithTracing(tracing => tracing
        .AddAspNetCoreInstrumentation()
        .AddHttpClientInstrumentation()
        .AddEntityFrameworkCoreInstrumentation()
        .AddSource("MyApp.Orders"))
    .WithMetrics(metrics => metrics
        .AddAspNetCoreInstrumentation()
        .AddHttpClientInstrumentation()
        .AddRuntimeInstrumentation()
        .AddMeter("MyApp.Orders"))
    .WithLogging(logging => logging
        .AddOtlpExporter());

// Cross-cutting OTLP export for traces + metrics (configured via env vars)
builder.Services.AddOpenTelemetry()
    .UseOtlpExporter();

The OTLP endpoint defaults to http://localhost:4317 (gRPC). Override via:

OTEL_EXPORTER_OTLP_ENDPOINT=http://collector:4317
OTEL_SERVICE_NAME=MyApp.Api

Custom Metrics with IMeterFactory

Register a metrics class as a singleton. IMeterFactory handles Meter disposal through DI.

public sealed class OrderMetrics
{
    private readonly Counter<int> _ordersCreated;
    private readonly Histogram<double> _orderDuration;
    private readonly UpDownCounter<int> _activeOrders;
    private readonly Gauge<double> _queueDepth;

    public OrderMetrics(IMeterFactory meterFactory)
    {
        var meter = meterFactory.Create("MyApp.Orders");

        _ordersCreated = meter.CreateCounter<int>(
            "myapp.orders.created", "{orders}", "Number of orders created");

        _orderDuration = meter.CreateHistogram<double>(
            "myapp.orders.duration", "s", "Order processing duration",
            advice: new InstrumentAdvice<double>
            {
                HistogramBucketBoundaries = [0.01, 0.05, 0.1, 0.5, 1, 5, 10]
            });

        _activeOrders = meter.CreateUpDownCounter<int>(
            "myapp.orders.active", "{orders}", "Currently active orders");

        _queueDepth = meter.CreateGauge<double>(
            "myapp.orders.queue_depth", "{items}", "Current queue depth");
    }

    public void OrderCreated() => _ordersCreated.Add(1);
    public void RecordDuration(double seconds) => _orderDuration.Record(seconds);
    public void OrderStarted() => _activeOrders.Add(1);
    public void OrderCompleted() => _activeOrders.Add(-1);
    public void SetQueueDepth(double depth) => _queueDepth.Record(depth);
}

// Registration
builder.Services.AddSingleton<OrderMetrics>();

Multi-Dimensional Metric Tags

Three or fewer tags are allocation-free. For more, use TagList.

// Allocation-free (3 or fewer tags)
_ordersCreated.Add(1,
    new KeyValuePair<string, object?>("order.type", "standard"),
    new KeyValuePair<string, object?>("payment.method", "credit_card"));

// 4+ tags — use TagList to avoid allocations
var tags = new TagList
{
    { "order.type", "standard" },
    { "payment.method", "credit_card" },
    { "region", "us-east" },
    { "priority", "high" }
};
_ordersCreated.Add(1, tags);

Custom ActivitySource for Distributed Tracing

public sealed class OrderService(ILogger<OrderService> logger)
{
    private static readonly ActivitySource Source = new("MyApp.Orders");

    public async Task<Order> ProcessOrderAsync(CreateOrderRequest request, CancellationToken ct)
    {
        using var activity = Source.StartActivity("ProcessOrder", ActivityKind.Internal);
        activity?.SetTag("order.customer_id", request.CustomerId);

        try
        {
            await ValidateOrder(request, ct);
            activity?.AddEvent(new ActivityEvent("OrderValidated"));

            var order = await SaveOrder(request, ct);
            activity?.SetTag("order.id", order.Id.ToString());
            activity?.SetStatus(ActivityStatusCode.Ok);
            return order;
        }
        catch (Exception ex)
        {
            activity?.SetStatus(ActivityStatusCode.Error, ex.Message);
            activity?.RecordException(ex);
            throw;
        }
    }
}

Register the source: .AddSource("MyApp.Orders") in the tracing builder.

Aspire Dashboard for Local Development

Run the standalone Aspire Dashboard without Aspire orchestration:

docker run --rm -it -p 18888:18888 -p 4317:18889 \
    mcr.microsoft.com/dotnet/aspire-dashboard:latest

Then point your app at it:

OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

Dashboard UI is at http://localhost:18888.

Source-Generated Logging with OTel

For maximum performance, use [LoggerMessage] — eliminates boxing and allocations.

public partial class OrderService(ILogger<OrderService> logger)
{
    [LoggerMessage(Level = LogLevel.Information,
        Message = "Processing order {OrderId} for customer {CustomerId}")]
    partial void LogOrderProcessing(Guid orderId, Guid customerId);
}

OpenTelemetry logging automatically includes TraceId and SpanId when an Activity is current.

Anti-patterns

Don't Create Meters Per Request

// BAD — new Meter per request causes memory leaks
public void HandleRequest()
{
    var meter = new Meter("MyApp");
    meter.CreateCounter<int>("requests").Add(1);
}

// GOOD — singleton via IMeterFactory
public class MyMetrics(IMeterFactory meterFactory)
{
    private readonly Counter<int> _requests =
        meterFactory.Create("MyApp").CreateCounter<int>("myapp.requests");
    public void RequestHandled() => _requests.Add(1);
}

Don't Skip Null Checks on Activity

// BAD — NullReferenceException when no listener is attached
using var activity = source.StartActivity("Work");
activity.SetTag("key", "value");

// GOOD — null-safe
activity?.SetTag("key", "value");

Don't Use High-Cardinality Metric Tags

// BAD — unbounded cardinality causes memory explosion in collectors
_counter.Add(1, new("request.id", Guid.NewGuid().ToString()));
_counter.Add(1, new("user.id", userId));

// GOOD — low-cardinality dimensions only
_counter.Add(1, new("http.method", "GET"), new("http.status_code", 200));

Don't Mix UseOtlpExporter with AddOtlpExporter

// BAD — throws NotSupportedException at runtime
builder.Services.AddOpenTelemetry()
    .UseOtlpExporter()
    .WithTracing(t => t.AddOtlpExporter());

// GOOD — use one approach
builder.Services.AddOpenTelemetry().UseOtlpExporter();

Don't Forget to Register Custom Sources

// BAD — activities silently dropped (no listener registered)
var source = new ActivitySource("MyApp.Custom");
using var activity = source.StartActivity("Work"); // null!

// GOOD — register in the tracing builder
otel.WithTracing(t => t.AddSource("MyApp.Custom"));
otel.WithMetrics(m => m.AddMeter("MyApp.Custom"));

Decision Guide

ScenarioRecommendation
Full observability setupAddOpenTelemetry() with all three signals + UseOtlpExporter()
Custom business metricsIMeterFactory + singleton metrics class
Custom trace spansActivitySource + StartActivity()
Local development backendAspire Dashboard standalone container
Production backendOTel Collector as intermediary to Grafana/Datadog/etc.
Sampling in productionOTEL_TRACES_SAMPLER=parentbased_traceidratio with 10% ratio
High-performance logging[LoggerMessage] source generator
Metric tag cardinalityMax ~1000 combinations per instrument
Environment configurationOTEL_* env vars (also work via appsettings.json)

平台分布

Codex

34.52%
按下载量换算60

Claude

29.47%
按下载量换算51

Cursor

19.51%
按下载量换算34

Gemini CLI

11.09%
按下载量换算19

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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