Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.

MITAuto-check passedDevOps & Cloud

Install Opentelemetry

skills CLI
$ npx skills add BagelHole/DevOps-Security-Agent-Skills --skill opentelemetry -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install BagelHole/DevOps-Security-Agent-Skills opentelemetry --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/devops/observability/opentelemetry .claude/skills/opentelemetry && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
opentelemetry
GitHub stars
1.2k
Token cost
~3.6k tokens
SKILL.md length
368 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
MIT

At a glance

Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.

  • Works in 5 steps: Define semantic conventions for… → Add SDK or auto-instrumentation in each… → Run an OpenTelemetry Collector to… → …
  • Implementing distributed tracing
  • SKILL.md covers When to Use This Skill, Prerequisites, Core Workflow and Collector Production…, plus 8 more sections
  • Reaches otel-collector.observability

What it does

Opentelemetry is an agent skill from BagelHole/DevOps-Security-Agent-Skills. Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. Use when implementing distributed tracing, service-level troubleshooting, or vendor-neutral observability.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Observability. It works with OpenTelemetry. The repository describes itself as: Agent-ready DevOps, security, infrastructure, and compliance knowledge base with 80+ skills across Kubernetes, Terraform, AWS/Azure/GCP, AI platform operations, container… The licence is MIT.

When your agent uses it

  • Implementing distributed tracing
  • Service-level troubleshooting
  • Vendor-neutral observability

Example prompts

  • “/opentelemetry”

Requirements

  • Python 3
  • Node.js

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Define semantic conventions for services, environments, and versions.
  2. Add SDK or auto-instrumentation in each service.
  3. Run an OpenTelemetry Collector to receive, transform, and export telemetry.
  4. Validate cardinality and sampling to control cost.
  5. Create golden signals dashboards and alerting from collected data.

What it can do on your machine

Read from SKILL.md and the folder at commit 0365f57. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, python and javascript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • otel-collector.observability

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Opentelemetry loads about 3.6k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 368 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from BagelHole/DevOps-Security-Agent-Skills at commit 0365f57, republished under its MIT licence (© BagelHole). 368 words, ~3,623 tokens.

Download SKILL.mdSave it as .claude/skills/opentelemetry/SKILL.md (or your agent's skills folder).
name
opentelemetry
description
Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. Use when implementing distributed tracing, service-level troubleshooting, or vendor-neutral observability.
license
MIT
metadata.author
devops-skills
metadata.version
1.0

OpenTelemetry

Adopt vendor-neutral telemetry with consistent instrumentation across services.

When to Use This Skill

  • Debugging latency across microservices
  • Standardizing observability data model and naming
  • Sending telemetry to Prometheus, Grafana, Datadog, or OTLP backends
  • Building SLO dashboards with trace-to-log correlation
  • Instrumenting Python or Node.js applications with tracing and metrics
  • Setting up auto-instrumentation for existing services without code changes

Prerequisites

  • Application services running in containers or on VMs
  • Backend for traces (Jaeger, Tempo, Datadog, or any OTLP receiver)
  • Backend for metrics (Prometheus, Mimir, or OTLP receiver)
  • Kubernetes cluster (for collector deployment) or VM with systemd
  • Network access from services to collector, and collector to backends

Core Workflow

  1. Define semantic conventions for services, environments, and versions.
  2. Add SDK or auto-instrumentation in each service.
  3. Run an OpenTelemetry Collector to receive, transform, and export telemetry.
  4. Validate cardinality and sampling to control cost.
  5. Create golden signals dashboards and alerting from collected data.

Collector Production Configuration

yaml
# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

  # Scrape Prometheus endpoints
  prometheus:
    config:
      scrape_configs:
        - job_name: "kubernetes-pods"
          kubernetes_sd_configs:
            - role: pod
          relabel_configs:
            - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
              action: keep
              regex: "true"
            - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_port]
              action: replace
              target_label: __address__
              regex: (.+)
              replacement: $$1

  # Host metrics for infrastructure monitoring
  hostmetrics:
    collection_interval: 30s
    scrapers:
      cpu: {}
      memory: {}
      disk: {}
      network: {}
      load: {}

processors:
  batch:
    send_batch_size: 1024
    timeout: 5s

  memory_limiter:
    check_interval: 1s
    limit_mib: 512
    spike_limit_mib: 128

  attributes:
    actions:
      - key: deployment.environment
        value: production
        action: upsert

  # Drop high-cardinality attributes to control cost
  filter/drop-debug:
    traces:
      span:
        - 'attributes["http.request.header.x-debug"] == "true"'

  # Reduce cardinality on URL paths
  transform/normalize-routes:
    trace_statements:
      - context: span
        statements:
          - replace_pattern(attributes["url.path"], "/users/[0-9]+", "/users/{id}")
          - replace_pattern(attributes["url.path"], "/orders/[0-9]+", "/orders/{id}")

  # Resource detection for cloud environments
  resourcedetection:
    detectors: [env, system, gcp, aws, azure]
    timeout: 5s

exporters:
  # Send traces to Tempo/Jaeger
  otlp/traces:
    endpoint: tempo:4317
    tls:
      insecure: true

  # Send metrics to Prometheus via remote write
  prometheusremotewrite:
    endpoint: http://mimir:9009/api/v1/push
    tls:
      insecure: true

  # Send logs to Loki
  otlp/logs:
    endpoint: loki:4317
    tls:
      insecure: true

  # Debug exporter for development
  debug:
    verbosity: basic

service:
  telemetry:
    logs:
      level: info
    metrics:
      address: 0.0.0.0:8888

  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, resourcedetection, transform/normalize-routes, batch, attributes]
      exporters: [otlp/traces]
    metrics:
      receivers: [otlp, prometheus, hostmetrics]
      processors: [memory_limiter, resourcedetection, batch, attributes]
      exporters: [prometheusremotewrite]
    logs:
      receivers: [otlp]
      processors: [memory_limiter, resourcedetection, batch, attributes]
      exporters: [otlp/logs]

Collector Kubernetes Deployment

yaml
# otel-collector-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: otel-collector
  namespace: observability
spec:
  replicas: 2
  selector:
    matchLabels:
      app: otel-collector
  template:
    metadata:
      labels:
        app: otel-collector
    spec:
      containers:
        - name: collector
          image: otel/opentelemetry-collector-contrib:0.98.0
          args: ["--config=/etc/otel/config.yaml"]
          ports:
            - containerPort: 4317
              name: otlp-grpc
            - containerPort: 4318
              name: otlp-http
            - containerPort: 8888
              name: metrics
          resources:
            requests:
              cpu: 200m
              memory: 256Mi
            limits:
              cpu: "1"
              memory: 512Mi
          volumeMounts:
            - name: config
              mountPath: /etc/otel
          livenessProbe:
            httpGet:
              path: /
              port: 13133
          readinessProbe:
            httpGet:
              path: /
              port: 13133
      volumes:
        - name: config
          configMap:
            name: otel-collector-config
---
apiVersion: v1
kind: Service
metadata:
  name: otel-collector
  namespace: observability
spec:
  selector:
    app: otel-collector
  ports:
    - name: otlp-grpc
      port: 4317
      targetPort: 4317
    - name: otlp-http
      port: 4318
      targetPort: 4318
    - name: metrics
      port: 8888
      targetPort: 8888

Python SDK Instrumentation

python
# tracing_setup.py
"""Initialize OpenTelemetry tracing and metrics for a Python service."""
from opentelemetry import trace, metrics
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.sdk.resources import Resource
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
import os

def init_telemetry(service_name: str, service_version: str):
    """Initialize OTel SDK with traces and metrics."""
    resource = Resource.create({
        "service.name": service_name,
        "service.version": service_version,
        "deployment.environment": os.getenv("DEPLOY_ENV", "development"),
    })

    # Traces
    trace_exporter = OTLPSpanExporter(
        endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://otel-collector:4317"),
        insecure=True,
    )
    tracer_provider = TracerProvider(resource=resource)
    tracer_provider.add_span_processor(BatchSpanProcessor(trace_exporter))
    trace.set_tracer_provider(tracer_provider)

    # Metrics
    metric_exporter = OTLPMetricExporter(
        endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://otel-collector:4317"),
        insecure=True,
    )
    metric_reader = PeriodicExportingMetricReader(metric_exporter, export_interval_millis=15000)
    meter_provider = MeterProvider(resource=resource, metric_readers=[metric_reader])
    metrics.set_meter_provider(meter_provider)

    # Auto-instrument common libraries
    RequestsInstrumentor().instrument()
    SQLAlchemyInstrumentor().instrument()

    return trace.get_tracer(service_name), metrics.get_meter(service_name)

# Usage example
tracer, meter = init_telemetry("order-service", "1.2.0")

# Custom span
with tracer.start_as_current_span("process_order") as span:
    span.set_attribute("order.id", order_id)
    span.set_attribute("order.total", total)
    # ... business logic ...

# Custom metric
request_counter = meter.create_counter(
    "app.requests",
    description="Total application requests",
)
request_counter.add(1, {"route": "/api/orders", "method": "POST"})

Node.js SDK Instrumentation

javascript
// tracing.js
// Initialize OpenTelemetry for a Node.js service.
// Load this file BEFORE any other imports: node -r ./tracing.js app.js
const { NodeSDK } = require("@opentelemetry/sdk-node");
const { OTLPTraceExporter } = require("@opentelemetry/exporter-trace-otlp-grpc");
const { OTLPMetricExporter } = require("@opentelemetry/exporter-metrics-otlp-grpc");
const { PeriodicExportingMetricReader } = require("@opentelemetry/sdk-metrics");
const { getNodeAutoInstrumentations } = require("@opentelemetry/auto-instrumentations-node");
const { Resource } = require("@opentelemetry/resources");
const { ATTR_SERVICE_NAME, ATTR_SERVICE_VERSION } = require("@opentelemetry/semantic-conventions");

const resource = new Resource({
  [ATTR_SERVICE_NAME]: process.env.SERVICE_NAME || "node-service",
  [ATTR_SERVICE_VERSION]: process.env.SERVICE_VERSION || "1.0.0",
  "deployment.environment": process.env.DEPLOY_ENV || "development",
});

const sdk = new NodeSDK({
  resource,
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || "http://otel-collector:4317",
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new OTLPMetricExporter({
      url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || "http://otel-collector:4317",
    }),
    exportIntervalMillis: 15000,
  }),
  instrumentations: [
    getNodeAutoInstrumentations({
      "@opentelemetry/instrumentation-http": {
        ignoreIncomingPaths: ["/health", "/ready"],
      },
      "@opentelemetry/instrumentation-express": { enabled: true },
      "@opentelemetry/instrumentation-pg": { enabled: true },
      "@opentelemetry/instrumentation-redis": { enabled: true },
    }),
  ],
});

sdk.start();
process.on("SIGTERM", () => sdk.shutdown());

Auto-Instrumentation with Kubernetes Operator

yaml
# otel-auto-instrumentation.yaml
# Install the OTel Operator first:
#   helm install opentelemetry-operator open-telemetry/opentelemetry-operator \
#     --namespace observability --create-namespace

# Define instrumentation for Python services
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: python-instrumentation
  namespace: default
spec:
  exporter:
    endpoint: http://otel-collector.observability:4317
  propagators:
    - tracecontext
    - baggage
  sampler:
    type: parentbased_traceidratio
    argument: "0.25"
  python:
    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-python:0.44b0
    env:
      - name: OTEL_PYTHON_LOG_CORRELATION
        value: "true"
---
# Define instrumentation for Node.js services
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: nodejs-instrumentation
  namespace: default
spec:
  exporter:
    endpoint: http://otel-collector.observability:4317
  propagators:
    - tracecontext
    - baggage
  sampler:
    type: parentbased_traceidratio
    argument: "0.25"
  nodejs:
    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-nodejs:0.49.1

To instrument a pod, add the annotation:

yaml
# For Python:
metadata:
  annotations:
    instrumentation.opentelemetry.io/inject-python: "true"

# For Node.js:
metadata:
  annotations:
    instrumentation.opentelemetry.io/inject-nodejs: "true"

Sampling Strategies

yaml
# Tail-based sampling config (in collector)
processors:
  tail_sampling:
    decision_wait: 10s
    num_traces: 100000
    policies:
      # Always keep error traces
      - name: errors
        type: status_code
        status_code:
          status_codes: [ERROR]

      # Always keep slow traces (> 2s)
      - name: slow-traces
        type: latency
        latency:
          threshold_ms: 2000

      # Sample 10% of successful traces
      - name: normal-traffic
        type: probabilistic
        probabilistic:
          sampling_percentage: 10

      # Always keep traces with specific attributes
      - name: important-users
        type: string_attribute
        string_attribute:
          key: user.tier
          values: [enterprise, premium]

      # Rate limit per service to prevent one service from dominating
      - name: rate-limit
        type: rate_limiting
        rate_limiting:
          spans_per_second: 500

Best Practices

  • Use tail-based sampling for high-volume production traces.
  • Tag telemetry with service.name, service.version, and deployment.environment.
  • Drop noisy attributes early in the collector.
  • Keep metric label cardinality low for stable query performance.
  • Use resource detectors to automatically populate cloud metadata.
  • Separate collector pools for traces vs metrics if volume requires it.
  • Set memory_limiter on every collector pipeline to prevent OOM.
  • Use the contrib collector image for production (includes more receivers/exporters).
Show full SKILL.md (122 more words)Show less

Troubleshooting

SymptomCheckFix
No traces arriving at backendCollector logs for export errorsVerify endpoint URL and network policy
Missing spans in a tracePropagation headers stripped by proxyConfigure proxy to pass traceparent header
High memory on collectorToo many in-flight traces for tail samplingReduce num_traces or increase memory limit
Metric cardinality explosionUnbounded label values (user IDs, URLs)Add transform processor to normalize values
Auto-instrumentation not workingPod annotation missing or operator not runningVerify operator is healthy and annotation is correct
Duplicate metricsBoth SDK and auto-instrumentation activeUse only one instrumentation method per signal

© BagelHole, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in devops/observability/opentelemetry of BagelHole/DevOps-Security-Agent-Skills.

Open the folder on GitHubat commit 0365f57

Compare with similar skills

Opentelemetry next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Opentelemetry compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Opentelemetry this skillBagelHole/DevOps-Security-Agent-Skills1.2k—~3.6kAutomated safety check: PassMIT
Motel Debugkitlangton/motel298—~2.2kAutomated safety check: PassMIT
Tempsgotempsh/temps831—~2kAutomated safety check: PassApache-2.0
Axiom Metrics Queryopenclaw/clawhub9.5k—~2.6kAutomated safety check: PassMIT
UModel Root Cause Analysisalibaba/UnifiedModel415—~1.9kAutomated safety check: PassCustom licence
Agent Kill Switchvivekchand/clawmetry426—~1.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Opentelemetry

What does Opentelemetry do?

Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. Opentelemetry is an agent skill from BagelHole/DevOps-Security-Agent-Skills. Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.

When should I use Opentelemetry?

Opentelemetry fits situations like: implementing distributed tracing; service-level troubleshooting; vendor-neutral observability.

How do I install Opentelemetry in Claude Code?

Run `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill opentelemetry -a claude-code`. Or copy the skill folder (devops/observability/opentelemetry in BagelHole/DevOps-Security-Agent-Skills) into .claude/skills/opentelemetry in your project. Claude Code loads it when a task matches its description.

How do I install Opentelemetry in Codex?

Run `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill opentelemetry -a codex`. Or copy the skill folder (devops/observability/opentelemetry in BagelHole/DevOps-Security-Agent-Skills) into .agents/skills/opentelemetry in your project. Codex loads it when a task matches its description.

Can I use Opentelemetry in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill opentelemetry -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/opentelemetry, .gemini/skills/opentelemetry, .github/skills/opentelemetry and .opencode/skills/opentelemetry in your project.

What does Opentelemetry need to run?

SKILL.md names no scripts, command-line tools or credentials: Opentelemetry is instructions for the agent only. Our summary lists: Python 3; Node.js.

Does Opentelemetry access the network?

SKILL.md names 1 domain. In commands or code: otel-collector.observability; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Opentelemetry safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Opentelemetry use?

Opentelemetry is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Opentelemetry use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Opentelemetry?

Skills that share tags, products or a category with Opentelemetry: Motel Debug (kitlangton/motel, 298 stars), Temps (gotempsh/temps, 831 stars), Axiom Metrics Query (openclaw/clawhub, 9.5k stars) and UModel Root Cause Analysis (alibaba/UnifiedModel, 415 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Opentelemetry?

BagelHole (a GitHub user) maintains it in BagelHole/DevOps-Security-Agent-Skills, which has 1,152 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on May 22, 2026.

Source: BagelHole/DevOps-Security-Agent-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.