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Configuring Opentelemetry Dotnet

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configuring-opentelemetry-dotnet

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.

Overview

Publisherdotnet
Repositoryskills
Skill nameconfiguring-opentelemetry-dotnet
Stars
5.4K
Forks
416
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by dotnet on GitHub. Read the source before you install it.

Installation

Install the Configuring Opentelemetry Dotnet AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dotnet/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/dotnet-aspnetcore/skills/configuring-opentelemetry-dotnet .claude/skills/configuring-opentelemetry-dotnet
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Configuring Opentelemetry Dotnet in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Configuring Opentelemetry Dotnet on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Configuring Opentelemetry Dotnet is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
# 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:

bash
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

csharp
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

csharp
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.

csharp
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:

csharp
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:

csharp
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

Frequently asked questions

What does the Configuring Opentelemetry Dotnet AI skill do?

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.

Why use Configuring Opentelemetry Dotnet on TypingMind?

Because you install it once and use it with any model. Configuring Opentelemetry Dotnet is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Configuring Opentelemetry Dotnet in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dotnet/skills/tree/main/plugins/dotnet-aspnetcore/skills/configuring-opentelemetry-dotnet. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Configuring Opentelemetry Dotnet?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Configuring Opentelemetry Dotnet?

As many as you like. As long as a model supports skills, you can use Configuring Opentelemetry Dotnet with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Configuring Opentelemetry Dotnet AI skill free?

Yes. It is published on GitHub by dotnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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