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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/dash0hq/agent-skills --skill otel-collector- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads otel-collector automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
OpenTelemetry Collector configuration guide
Expert guidance for configuring and deploying the OpenTelemetry Collector to receive, process, and export telemetry.
Rules
| Rule | Description |
|---|---|
| receivers | Receivers — OTLP, Prometheus, filelog, hostmetrics |
| exporters | Exporters — OTLP/gRPC to Dash0, debug, authentication |
| processors | Processors — memory limiter, resource detection, ordering, sending queue |
| pipelines | Pipelines — service section, per-signal configuration, connectors |
| deployment | Deployment — agent vs gateway patterns, deployment method selection |
| dash0-operator | Dash0 Kubernetes Operator — automated instrumentation, Collector management, Dash0 export |
| collector-helm-chart | Collector Helm chart — presets, modes, image selection |
| opentelemetry-operator | OpenTelemetry Operator — Collector CRD, auto-instrumentation, sidecar |
| raw-manifests | Raw Kubernetes manifests — DaemonSet, Deployment, RBAC, Docker Compose |
| sampling | Sampling — head, tail, load balancing |
| red-metrics | RED metrics — span-derived request rate, error rate, duration histograms |
| custom-distributions | Custom distributions — building a stripped-down Collector binary with OCB |
Key principles
- Processor ordering matters.
Place
memory_limiterfirst in every pipeline. Enable exporter batching withsending_queue: { batch: {} }and persist the queue withfile_storageinstead of using thebatchprocessor. 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_detectionandk8s_attributesto every signal pipeline (traces, metrics, and logs), not just one. If one pipeline enriches telemetry withk8s.namespace.nameorhost.namebut 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, andresource_detectionas config keys. The older namesotlp(exporter only),otlphttp,loadbalancing,spanmetrics,logdedup,signaltometrics,k8sattributes, andresourcedetectionstill 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_balancingandresource_detectionexist only from v0.153.0, andsending_queue.batch: {}only from v0.138.0. An older Collector rejects them at startup:unknown typefor a new name, and forbatch: {}either'sending_queue' has invalid keys: batchor`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_limiterin 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.
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
- Write config — define receivers, processors, and exporters; wire them in
service.pipelines. - Validate locally — run
otelcol validate --config=config.yamlto catch structural errors before deployment. - Deploy — choose a deployment method from the deployment rule (Helm, Operator, raw manifests, or Docker Compose).
- Verify — add the
debugexporter 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 applications | receivers |
| Scrape Prometheus endpoints | receivers |
| Collect log files or host metrics | receivers |
| Send telemetry to Dash0 | exporters |
| Configure retry, queue, or compression | exporters |
| Set processor ordering | processors |
| Add Kubernetes or cloud metadata | processors |
| Wire receivers → processors → exporters | pipelines |
| Complete working configuration | pipelines |
| Validate the pipeline with the debug exporter | collector-helm-chart, opentelemetry-operator, raw-manifests, or dash0-operator |
| Deploy as DaemonSet or Deployment | raw-manifests |
| Deploy with Helm | collector-helm-chart |
| Deploy with the OTel Operator | opentelemetry-operator |
| Deploy with the Dash0 Operator | dash0-operator |
| Auto-instrument applications in Kubernetes | opentelemetry-operator or dash0-operator |
| Local development with Docker Compose | raw-manifests |
| Reduce trace volume | sampling |
| Keep errors and slow traces, drop the rest | sampling |
| Redact sensitive data in the pipeline | processors |
| Generate RED metrics from traces | red-metrics |
| Build a custom Collector binary | custom-distributions |
Official documentation
FAQ
What does the otel-collector 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.
How do I install otel-collector?
Run npx skills add https://github.com/dash0hq/agent-skills --skill otel-collector in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/otel-collector/ (personal) or .claude/skills/otel-collector/ (project).
Which AI agents can use otel-collector?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is otel-collector safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains otel-collector?
It is published by dash0hq in dash0hq/agent-skills, and it is an official skill from the maker of the technology it covers. It has 666 recorded installs.
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