Agent skill

Opentelemetry

by sickn33 in sickn33/agentic-awesome-skills

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

MITAuto-check passedDevOps & Cloud

Install Opentelemetry

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill opentelemetry -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/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
47k
Used in
1 other repo
Token cost
~3.2k tokens
SKILL.md length
237 words
Files
2 (incl. references)
Skills in repo
1,493
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… → …
  • Tasks that involve Observability
  • SKILL.md covers Prerequisites, Core Workflow, Collector Production… and Collector Kubernetes Deployment, plus 6 more sections
  • Calls git and kubectl; reaches otel-collector.observability

What it does

Opentelemetry is an agent skill from sickn33/agentic-awesome-skills. Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/details.md`). Compatibility notes: Requires the relevant platform CLIs (kubectl, helm, terraform, git, CI runners) and authorized access to the target environment. Docs-only; helper scripts and…

It sits in DevOps & Cloud, covering Observability. It works with OpenTelemetry. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Observability

Example prompts

  • “/opentelemetry”

Requirements

  • Python 3
  • Node.js
  • Compatibility (from SKILL.md): Requires the relevant platform CLIs (kubectl, helm, terraform, git, CI runners) and authorized access to the target environment. Docs-only; helper scripts and templates not bundled.

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 680176d. 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

    Shell commands in SKILL.md call:

    • git
    • kubectl

    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

    Also links to:

    • github.com

    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.

  • Compatibility

    Requires the relevant platform CLIs (kubectl, helm, terraform, git, CI runners) and authorized access to the target environment. Docs-only; helper scripts and templates not bundled.

    From compatibility in the SKILL.md frontmatter.

Context cost

Opentelemetry loads about 3.2k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 29 tokens; SKILL.md has 237 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

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 sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 237 words, ~3,237 tokens.

Download SKILL.mdSave it as .claude/skills/opentelemetry/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
opentelemetry
description
Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.
compatibility
Requires the relevant platform CLIs (kubectl, helm, terraform, git, CI runners) and authorized access to the target environment. Docs-only; helper scripts and templates not bundled.
category
devops
risk
critical
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
1.0

OpenTelemetry

Adopt vendor-neutral telemetry with consistent instrumentation across services.

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"

Contents

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

Limitations

  • Guidance executes against real environments: confirm target, blast radius, and rollback plan before applying anything.
  • Never deploy to production without explicit approval. Docs-only import: upstream scripts and templates not bundled.
Example
bash
git status && git diff --stat
kubectl diff -f manifest.yaml

Adapted from BagelHole/DevOps-Security-Agent-Skills (MIT); frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import: helper scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/opentelemetry of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/details.md

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

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 skillsickn33/agentic-awesome-skills47k1 repos~3.2kAutomated safety check: PassMIT
Motel Debugkitlangton/motel298—~2.2kAutomated safety check: PassMIT
Tempsgotempsh/temps828—~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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  • Motel Debug

    kitlangton/motel

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    298 GitHub stars~2.2k tokensUpdated 1 mo ago
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    openclaw/clawhub

    Explores and queries OpenTelemetry metrics in Axiom MetricsDB, listing datasets, metrics and tags first and picking the right aggregation for each metric's type.

    9.5k GitHub stars~2.6k tokensUpdated today
    DevOps & CloudAuto-check passed
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    415 GitHub stars~1.9k tokensUpdated 15 days ago
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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 sickn33/agentic-awesome-skills. Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.

When should I use Opentelemetry?

Opentelemetry fits situations like: tasks that involve Observability.

How do I install Opentelemetry in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill opentelemetry -a claude-code`. Or copy the skill folder (skills/opentelemetry in sickn33/agentic-awesome-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 sickn33/agentic-awesome-skills --skill opentelemetry -a codex`. Or copy the skill folder (skills/opentelemetry in sickn33/agentic-awesome-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 sickn33/agentic-awesome-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?

Going by SKILL.md and its folder, Opentelemetry needs the command-line tools its instructions call (git and kubectl). Our summary lists: Python 3; Node.js. Compatibility (from SKILL.md): Requires the relevant platform CLIs (kubectl, helm, terraform, git, CI runners) and authorized access to the target environment. Docs-only; helper scripts and templates not bundled..

Does Opentelemetry access the network?

SKILL.md names 2 domains. In commands or code: otel-collector.observability; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. 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 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 718 tokens, read only when the agent opens those files.

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, 828 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?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.