configuring-opentelemetry-dotnet

dotnet/skills · updated May 23, 2026

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$npx skills add https://github.com/dotnet/skills --skill configuring-opentelemetry-dotnet
0 commentsdiscussion
summary

Configure OpenTelemetry distributed tracing, metrics, and logging in ASP.NET Core using the .NET OpenTelemetry SDK. Use when adding observability, setting up OTLP exporters, creating custom metrics/spans, or troubleshooting distributed trace correlation.

skill.md
name
configuring-opentelemetry-dotnet
description
Configure OpenTelemetry distributed tracing, metrics, and logging in ASP.NET Core using the .NET OpenTelemetry SDK. Use when adding observability, setting up OTLP exporters, creating custom metrics/spans, or troubleshooting distributed trace correlation.
license
MIT

Configuring OpenTelemetry in .NET

When to Use

  • Adding distributed tracing to an ASP.NET Core application
  • Setting up OpenTelemetry exporters (OTLP is the primary protocol; Jaeger accepts OTLP natively; Prometheus OTLP ingestion requires explicit opt-in)
  • Creating custom metrics or trace spans for business operations
  • Troubleshooting distributed trace context propagation across services

When Not to Use

  • The user wants application-level logging only (use ILogger, Serilog)
  • The user is using Application Insights SDK directly (different API)
  • The user needs APM with a commercial vendor's proprietary SDK

Inputs

InputRequiredDescription
ASP.NET Core projectYesThe application to instrument
Observability backendNoWhere to export: OTLP collector, Aspire dashboard, Jaeger (accepts OTLP natively)

Workflow

Step 1: Install the correct packages

There are many OpenTelemetry NuGet packages. Install exactly these:

# Core SDK + ASP.NET Core instrumentation + logging integration
dotnet add package OpenTelemetry.Extensions.Hosting
dotnet add package OpenTelemetry.Instrumentation.AspNetCore
dotnet add package OpenTelemetry.Instrumentation.Http

# Exporter
dotnet add package OpenTelemetry.Exporter.OpenTelemetryProtocol  # OTLP exporter for traces, metrics, AND logs

# Optional — dev/local debugging only (do NOT include in production deployments)
# dotnet add package OpenTelemetry.Exporter.Console

Do NOT install OpenTelemetry alone — you need OpenTelemetry.Extensions.Hosting for proper DI integration.

Optional: additional auto-instrumentation packages

Install only the packages that match the libraries your application uses:

dotnet add package OpenTelemetry.Instrumentation.SqlClient           # SQL Server queries
dotnet add package OpenTelemetry.Instrumentation.EntityFrameworkCore  # EF Core
dotnet add package OpenTelemetry.Instrumentation.GrpcNetClient       # gRPC calls
dotnet add package OpenTelemetry.Instrumentation.Runtime             # GC, thread pool metrics

Step 2: Configure all signals in Program.cs

using OpenTelemetry.Resources;
using OpenTelemetry.Trace;
using OpenTelemetry.Metrics;
using OpenTelemetry.Logs;

var builder = WebApplication.CreateBuilder(args);

builder.Services.AddOpenTelemetry()
    .ConfigureResource(resource => resource
        .AddService(serviceName: builder.Environment.ApplicationName))
    .WithTracing(tracing => tracing
        .AddAspNetCoreInstrumentation(options =>
        {
            // Filter out health check endpoints from traces
            options.Filter = httpContext =>
                !httpContext.Request.Path.StartsWithSegments("/healthz");
        })
        .AddHttpClientInstrumentation(options =>
        {
            options.RecordException = true;
        })
        // Optional: add SQL instrumentation if using SqlClient directly
        // .AddSqlClientInstrumentation(options =>
        // {
        //     options.SetDbStatementForText = true;
        //     options.RecordException = true;
        // })
        // Custom activity sources (must match ActivitySource names in your code)
        .AddSource("MyApp.Orders")
        .AddSource("MyApp.Payments")
        .AddSource("MyApp.Messaging"))
    .WithMetrics(metrics => metrics
        .AddAspNetCoreInstrumentation()
        .AddHttpClientInstrumentation()
        // Optional: .AddRuntimeInstrumentation() for GC and thread pool metrics
        //   (requires OpenTelemetry.Instrumentation.Runtime package)
        // Custom meters (must match Meter names in your code)
        .AddMeter("MyApp.Metrics"))
    .WithLogging(logging =>
    {
        logging.IncludeScopes = true;
        // logging.IncludeFormattedMessage = true;  // Enable if you need the formatted message string in log exports
    })
    // Single OTLP exporter for all signals — reads OTEL_EXPORTER_OTLP_ENDPOINT
    // env var (defaults to http://localhost:4317). Override via environment variable
    // or appsettings.json configuration.
    .UseOtlpExporter();

Step 3: Understanding log–trace correlation

The .WithLogging() call in Step 2 integrates ILogger with OpenTelemetry:

  • Each log entry automatically includes TraceId and SpanId for correlation with traces
  • The service resource from .ConfigureResource() propagates to logs automatically
  • UseOtlpExporter() applies to logs alongside traces and metrics
  • No additional packages or separate SetResourceBuilder call needed

Step 4: Create custom spans (Activities) for business operations

using System.Diagnostics;
using Microsoft.Extensions.Logging;

public class OrderService
{
    // Create an ActivitySource matching what you registered in Step 2
    private static readonly ActivitySource ActivitySource = new("MyApp.Orders");
    private readonly ILogger<OrderService> _logger;

    public OrderService(ILogger<OrderService> logger) => _logger = logger;

    public async Task<Order> ProcessOrderAsync(CreateOrderRequest request)
    {
        // Start a new span
        using var activity = ActivitySource.StartActivity("ProcessOrder");

        // Add attributes (tags) to the span
        activity?.SetTag("order.customer_id", request.CustomerId);
        activity?.SetTag("order.item_count", request.Items.Count);

        try
        {
            // Child span for validation
            using (var validationActivity = ActivitySource.StartActivity("ValidateOrder"))
            {
                await ValidateOrderAsync(request);
                validationActivity?.SetTag("validation.result", "passed");
            }

            // Child span for payment
            using (var paymentActivity = ActivitySource.StartActivity("ProcessPayment",
                ActivityKind.Client))  // Client = outgoing call
            {
                paymentActivity?.SetTag("payment.method", request.PaymentMethod);
                await ProcessPaymentAsync(request);
            }

            var order = new Order { Id = Guid.NewGuid(), CustomerId = request.CustomerId, Status = "Completed" };

            activity?.SetTag("order.status", "completed");
            activity?.SetStatus(ActivityStatusCode.Ok);

            return order;
        }
        catch (Exception ex)
        {
            activity?.SetStatus(ActivityStatusCode.Error, ex.Message);
            // Log via ILogger — OpenTelemetry captures this with trace correlation.
            // Prefer logging over activity.RecordException() as OTel is deprecating
            // span events for exception recording in favor of log-based exceptions.
            _logger.LogError(ex, "Order processing failed for customer {CustomerId}", request.CustomerId);
            throw;
        }
    }
}

Critical: ActivitySource name must match AddSource("...") in configuration. Unmatched sources are silently ignored — this is the #1 debugging issue.

Step 5: Create custom metrics

Use IMeterFactory (injected via DI) to create meters — this ensures proper lifetime management and testability.

using System.Diagnostics;
using System.Diagnostics.Metrics;

public class OrderMetrics
{
    private readonly Counter<long> _ordersProcessed;
    private readonly Histogram<double> _orderProcessingDuration;
    private readonly UpDownCounter<int> _activeOrders;

    public OrderMetrics(IMeterFactory meterFactory)
    {
        // Meter name must match AddMeter("...") in configuration
        var meter = meterFactory.Create("MyApp.Metrics");

        // Counter — use for things that only go up
        _ordersProcessed = meter.CreateCounter<long>(
            "orders.processed", "orders", "Total orders successfully processed");

        // Histogram — use for measuring distributions (latency, sizes)
        _orderProcessingDuration = meter.CreateHistogram<double>(
            "orders.processing_duration", "ms", "Time to process an order");

        // UpDownCounter — use for things that go up AND down
        _activeOrders = meter.CreateUpDownCounter<int>(
            "orders.active", "orders", "Currently processing orders");
    }

    public void RecordOrderProcessed(string region, double durationMs)
    {
        // Tags enable dimensional filtering (by region, status, etc.)
        var tags = new TagList
        {
            { "region", region },
            { "order.type", "standard" }
        };

        _ordersProcessed.Add(1, tags);
        _orderProcessingDuration.Record(durationMs, tags);
    }
}

Register OrderMetrics in DI:

builder.Services.AddSingleton<OrderMetrics>();

Step 6: Configure context propagation for distributed scenarios

Trace context propagation is automatic for HTTP calls when using AddHttpClientInstrumentation(). For non-HTTP scenarios:

using System;
using System.Collections.Generic;
using System.Diagnostics;
using OpenTelemetry.Context.Propagation;

// ActivitySource should be static — register via .AddSource("MyApp.Messaging") in Step 2
private static readonly ActivitySource MessageSource = new("MyApp.Messaging");

// Manual context propagation (e.g., across message queues)
// On the SENDING side:
var propagator = Propagators.DefaultTextMapPropagator;
var activityContext = Activity.Current?.Context ?? default;
var context = new PropagationContext(activityContext, Baggage.Current);
var carrier = new Dictionary<string, string>();

propagator.Inject(context, carrier, (dict, key, value) => dict[key] = value);
// Send carrier dictionary as message headers

// On the RECEIVING side:
var parentContext = propagator.Extract(default, carrier,
    (dict, key) => dict.TryGetValue(key, out var value) ? new[] { value } : Array.Empty<string>());

Baggage.Current = parentContext.Baggage;
using var activity = MessageSource.StartActivity("ProcessMessage",
    ActivityKind.Consumer,
    parentContext.ActivityContext);  // Links to parent trace!

Validation

  • Traces appear in the observability backend (Jaeger, Aspire dashboard, etc.)
  • HTTP requests automatically create spans with correct verb, URL, status code
  • Custom ActivitySource names match AddSource() registrations
  • Custom Meter names match AddMeter() registrations
  • Logs include TraceId and SpanId for correlation
  • Health check endpoints are filtered from traces
  • Exception details appear on error spans

Common Pitfalls

PitfallSolution
ActivitySource.StartActivity returns nullSource name doesn't match any AddSource() — names must match exactly
Traces not appearing in exporterCheck OTLP endpoint: gRPC uses port 4317, HTTP uses 4318
Missing HTTP client spansEnsure AddHttpClientInstrumentation() is registered; it works for both IHttpClientFactory/DI and new HttpClient() (use IHttpClientFactory for lifetime management)
High cardinality tagsDon't use user IDs, request IDs, or UUIDs as metric tags — explodes storage
OTLP gRPC vs HTTP mismatchDefault is gRPC (port 4317); if collector only accepts HTTP, set OtlpExportProtocol.HttpProtobuf
Meter / ActivitySource lifecycleActivitySource should be static; create Meter via IMeterFactory from DI (not new Meter()) for proper lifetime management and testability
how to use configuring-opentelemetry-dotnet

How to use configuring-opentelemetry-dotnet on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add configuring-opentelemetry-dotnet
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/dotnet/skills --skill configuring-opentelemetry-dotnet

The skills CLI fetches configuring-opentelemetry-dotnet from GitHub repository dotnet/skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/configuring-opentelemetry-dotnet

Reload or restart Cursor to activate configuring-opentelemetry-dotnet. Access the skill through slash commands (e.g., /configuring-opentelemetry-dotnet) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.646 reviews
  • Amelia Khan· Dec 28, 2024

    Registry listing for configuring-opentelemetry-dotnet matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Dec 20, 2024

    Keeps context tight: configuring-opentelemetry-dotnet is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Charlotte Martin· Dec 16, 2024

    Useful defaults in configuring-opentelemetry-dotnet — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Advait Malhotra· Dec 16, 2024

    Solid pick for teams standardizing on skills: configuring-opentelemetry-dotnet is focused, and the summary matches what you get after install.

  • Aisha Verma· Nov 19, 2024

    configuring-opentelemetry-dotnet fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 11, 2024

    configuring-opentelemetry-dotnet has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Sofia Iyer· Nov 7, 2024

    configuring-opentelemetry-dotnet is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Carlos Malhotra· Oct 26, 2024

    Keeps context tight: configuring-opentelemetry-dotnet is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Daniel Ghosh· Oct 10, 2024

    We added configuring-opentelemetry-dotnet from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dhruvi Jain· Oct 2, 2024

    Solid pick for teams standardizing on skills: configuring-opentelemetry-dotnet is focused, and the summary matches what you get after install.

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