Otel Collector logo

Otel Collector

Organization
dash0hq
otel-collector

Expert guidance for configuring and deploying the OpenTelemetry Collector. Use when setting up a Collector pipeline, configuring receivers, exporters, or processors, deploying a Collector to Kubernetes or Docker, or forwarding telemetry to Dash0. Triggers on requests involving collector, pipeline, OTLP receiver, exporter, or Dash0 collector setup.

Overview

Publisherdash0hq
Repositoryagent-skills
Skill nameotel-collector
Stars
98
Forks
16
Bundled files
12
LicenseApache-2.0
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.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dash0hq on GitHub. Read the source before you install it.

Installation

Install the Otel Collector 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.
    https://github.com/dash0hq/agent-skills/tree/main/skills/otel-collector
  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/dash0hq/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/otel-collector .claude/skills/otel-collector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Otel Collector 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 Otel Collector 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 Otel Collector 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.

OpenTelemetry Collector configuration guide

Expert guidance for configuring and deploying the OpenTelemetry Collector to receive, process, and export telemetry.

Rules

RuleDescription
receiversReceivers — OTLP, Prometheus, filelog, hostmetrics
exportersExporters — OTLP/gRPC to Dash0, debug, authentication
processorsProcessors — memory limiter, resource detection, ordering, sending queue
pipelinesPipelines — service section, per-signal configuration, connectors
deploymentDeployment — agent vs gateway patterns, deployment method selection
dash0-operatorDash0 Kubernetes Operator — automated instrumentation, Collector management, Dash0 export
collector-helm-chartCollector Helm chart — presets, modes, image selection
opentelemetry-operatorOpenTelemetry Operator — Collector CRD, auto-instrumentation, sidecar
raw-manifestsRaw Kubernetes manifests — DaemonSet, Deployment, RBAC, Docker Compose
samplingSampling — head, tail, load balancing
red-metricsRED metrics — span-derived request rate, error rate, duration histograms
custom-distributionsCustom distributions — building a stripped-down Collector binary with OCB

Key principles

  • Processor ordering matters. Place memory_limiter first in every pipeline. Enable exporter batching with sending_queue: { batch: {} } and persist the queue with file_storage instead of using the batch processor. Incorrect ordering causes memory exhaustion or data loss.
  • One pipeline per signal type. Define separate pipelines for traces, metrics, and logs. Mixing signals in a single pipeline breaks processing and causes runtime errors.
  • Every declared component must appear in a pipeline. The Collector ignores components that no pipeline references, without an error or warning, so an unwired component does nothing.
  • Consistent resource enrichment across pipelines. Apply processors that enrich resource attributes like resource_detection and k8s_attributes to every signal pipeline (traces, metrics, and logs), not just one. If one pipeline enriches telemetry with k8s.namespace.name or host.name but another does not, correlation between signals is compromised by incomplete metadata.
  • Use canonical component names. Write otlp_grpc, otlp_http, load_balancing, span_metrics, log_dedup, signal_to_metrics, k8s_attributes, and resource_detection as config keys. The older names otlp (exporter only), otlphttp, loadbalancing, spanmetrics, logdedup, signaltometrics, k8sattributes, and resourcedetection still work as deprecated aliases and log a deprecation warning at startup.
  • Run Collector v0.153.0 or later. The configs in this skill need it: load_balancing and resource_detection exist only from v0.153.0, and sending_queue.batch: {} only from v0.138.0. An older Collector rejects them at startup: unknown type for a new name, and for batch: {} either 'sending_queue' has invalid keys: batch or `batch` supports only `items` or `bytes` sizer. When you pin an image, pin the version the examples are written against, v0.163.1.
  • Memory safety is non-negotiable. Always configure memory_limiter in production. Without it, a burst of telemetry can cause the Collector to OOM and crash.

Quick start

Minimal working configuration: OTLP receiver → memory limiter → OTLP/gRPC exporter to Dash0.

yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  memory_limiter:
    check_interval: 1s
    limit_mib: 400
    spike_limit_mib: 100

exporters:
  otlp_grpc:
    endpoint: ingress.eu-west-1.aws.dash0.com:4317
    headers:
      Authorization: "Bearer ${env:DASH0_TOKEN}"
    sending_queue:
      enabled: true
      storage: file_storage
      batch: {}

extensions:
  file_storage:
    directory: /var/lib/otelcol/queue
    create_directory: true

service:
  extensions: [file_storage]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter]
      exporters: [otlp_grpc]
    metrics:
      receivers: [otlp]
      processors: [memory_limiter]
      exporters: [otlp_grpc]
    logs:
      receivers: [otlp]
      processors: [memory_limiter]
      exporters: [otlp_grpc]

The Collector user must be able to write to the file_storage directory; in a container, mount a persistent volume at /var/lib/otelcol.

See exporters for full authentication and queue configuration, and processors for adding resource detection.

Configuration workflow

  1. Write config — define receivers, processors, and exporters; wire them in service.pipelines.
  2. Validate locally — run otelcol validate --config=config.yaml to catch structural errors before deployment.
  3. Deploy — choose a deployment method from the deployment rule (Helm, Operator, raw manifests, or Docker Compose).
  4. Verify — add the debug exporter to a pipeline temporarily and inspect stdout to confirm telemetry is flowing; then remove it before going to production.

Quick reference

What do you need?Rule
Accept OTLP telemetry from applicationsreceivers
Scrape Prometheus endpointsreceivers
Collect log files or host metricsreceivers
Send telemetry to Dash0exporters
Configure retry, queue, or compressionexporters
Set processor orderingprocessors
Add Kubernetes or cloud metadataprocessors
Wire receivers → processors → exporterspipelines
Complete working configurationpipelines
Validate the pipeline with the debug exportercollector-helm-chart, opentelemetry-operator, raw-manifests, or dash0-operator
Deploy as DaemonSet or Deploymentraw-manifests
Deploy with Helmcollector-helm-chart
Deploy with the OTel Operatoropentelemetry-operator
Deploy with the Dash0 Operatordash0-operator
Auto-instrument applications in Kubernetesopentelemetry-operator or dash0-operator
Local development with Docker Composeraw-manifests
Reduce trace volumesampling
Keep errors and slow traces, drop the restsampling
Redact sensitive data in the pipelineprocessors
Generate RED metrics from tracesred-metrics
Build a custom Collector binarycustom-distributions

Official documentation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Otel Collector AI skill do?

Expert guidance for configuring and deploying the OpenTelemetry Collector. Use when setting up a Collector pipeline, configuring receivers, exporters, or processors, deploying a Collector to Kubernetes or Docker, or forwarding telemetry to Dash0. Triggers on requests involving collector, pipeline, OTLP receiver, exporter, or Dash0 collector setup.

Why use Otel Collector on TypingMind?

Because you install it once and use it with any model. Otel Collector 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 Otel Collector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dash0hq/agent-skills/tree/main/skills/otel-collector. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Otel Collector?

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 Otel Collector?

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

Is the Otel Collector AI skill free?

Yes. It is published on GitHub by dash0hq under the Apache-2.0 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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