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Grafana opentelemetry

Skill Jylhis/skills/skills/services/grafana-opentelemetry

Instrument any app with OpenTelemetry and ship metrics / logs / traces to Grafana Cloud or self-hosted Mimir / Loki / Tempo / Pyroscope. Covers SDK auto-instrumentation for Go, Java (Grafana JVM agent), Python (`opentelemetry-instrument`), Node.js, .NET (`Grafana.OpenTelemetry`), Beyla eBPF for zero-code; Grafana Cloud OTLP gateway + Basic-auth (instanceID + API key, base64); env-var config (`OTEL_EXPORTER_OTLP_*`, `OTEL_RESOURCE_ATTRIBUTES`); Alloy / OTel-Collector pipelines; Kubernetes Operator inject-annotations; and head + tail sampling. Use when instrumenting a service, pointing OTLP at Grafana Cloud, switching from Jaeger / Datadog / New Relic, choosing head- vs tail-sampling, or debugging "spans aren't showing in Explore" — even when the user says "auto-instrument my Java app", "send traces to Grafana", "what env vars do I set", "OTLP endpoint", or "Operator inject" without naming OpenTelemetry.From its SKILL.md

Install
npx -y skills add Jylhis/skills --skill grafana-opentelemetry

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SKILL.md

6.9 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it

OpenTelemetry with Grafana

Docs: https://grafana.com/docs/opentelemetry/

Vendor-neutral instrumentation pipeline. Apps speak OTLP → Alloy (or direct) → Grafana Cloud (Mimir / Loki / Tempo / Pyroscope).

Backends

SignalBackend
MetricsGrafana Mimir
LogsGrafana Loki
TracesGrafana Tempo
ProfilesGrafana Pyroscope

Prerequisites

  • Grafana Cloud stack OR self-hosted Mimir / Loki / Tempo
  • Cloud OTLP endpoint: https://otlp-gateway-<region>.grafana.net/otlp
  • Basic-auth credentials: numeric instance ID + API token with MetricsPublisher + LogsPublisher + TracesPublisher
  • An app to instrument

Common Workflows

1. Authenticate to the Grafana Cloud OTLP endpoint

# 1. Build the auth header
INSTANCE_ID=123456
API_KEY="glc_eyJ..."
export OTEL_EXPORTER_OTLP_ENDPOINT=https://otlp-gateway-prod-us-east-0.grafana.net/otlp
export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Basic $(echo -n "${INSTANCE_ID}:${API_KEY}" | base64)"
export OTEL_RESOURCE_ATTRIBUTES="service.name=myapp,service.namespace=myteam,deployment.environment=prod"

# 2. Smoke-test creds with a curl POST against the OTLP traces endpoint (empty body)
curl -s -o /dev/null -w "%{http_code}\n" \
  -X POST -H "Content-Type: application/x-protobuf" \
  -H "Authorization: Basic $(echo -n "${INSTANCE_ID}:${API_KEY}" | base64)" \
  "$OTEL_EXPORTER_OTLP_ENDPOINT/v1/traces" --data-binary '\n'
# Expect 400 (malformed payload) — NOT 401 (auth) or 404 (wrong endpoint).

2. Auto-instrument a Java app + verify

# 1. Download the Grafana JVM agent (single jar)
curl -sLO https://github.com/grafana/grafana-opentelemetry-java/releases/latest/download/grafana-opentelemetry-java.jar

# 2. Run with the agent + env from step 1
java -javaagent:./grafana-opentelemetry-java.jar -jar myapp.jar

# 3. Generate traffic, then verify in Grafana → Explore → Tempo:
#    TraceQL: { resource.service.name = "myapp" }
#    Expect spans within ~30s. Also verify metrics:
#    PromQL: count by (service_name)({service_name="myapp"})

3. Auto-instrument a Python app

pip install "opentelemetry-distro[otlp]"
opentelemetry-bootstrap -a install

# Same env vars as step 1, then:
opentelemetry-instrument python app.py

# Verify the same way — Explore → Traces filter service.name=myapp.

4. Add Alloy as a buffering / sampling collector

# Application points at local Alloy (gRPC fastest)
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
export OTEL_EXPORTER_OTLP_PROTOCOL=grpc

# Alloy environment for forwarding to Cloud
export GRAFANA_CLOUD_OTLP_ENDPOINT=https://otlp-gateway-prod-us-east-0.grafana.net/otlp
export GRAFANA_CLOUD_INSTANCE_ID=$INSTANCE_ID
export GRAFANA_CLOUD_API_KEY=$API_KEY
alloy run /etc/alloy/config.alloy

# Verify Alloy received and forwarded
curl -s http://localhost:12345/metrics | grep otelcol_exporter_sent_spans

Full Alloy config + tail-sampling block + OTel Collector YAML + K8s Operator install: references/collector-config.md.

SDK-by-language details (Go full code, Node manual setup, .NET ASP.NET Core, all the env-var quirks): references/instrumentation.md.

5. Kubernetes — auto-inject via the Operator

apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata: { name: my-instrumentation }
spec:
  exporter: { endpoint: http://otelcol:4317 }
  propagators: [tracecontext, baggage]
  java:
    image: us-docker.pkg.dev/grafanalabs-global/docker-grafana-opentelemetry-java-prod/grafana-opentelemetry-java:2.3.0-beta.1
  nodejs: {}
  python: {}

Then annotate pods:

metadata:
  annotations:
    instrumentation.opentelemetry.io/inject-java: "true"
    # or: inject-nodejs, inject-python, inject-dotnet
# Verify the operator injected the agent
kubectl describe pod <pod> | grep -A2 'opentelemetry-auto-instrumentation'
# Then run the same Grafana Explore checks.

Sampling — when to pick which

# Head sampling (cheap, decided at start; may lose rare errors)
export OTEL_TRACES_SAMPLER=parentbased_traceidratio
export OTEL_TRACES_SAMPLER_ARG=0.1   # 10%

Tail sampling (decides after seeing the whole trace — keep errors + sample the rest) requires an Alloy / OTel-Collector tail_sampling processor; full block in references/collector-config.md.

Key environment variables

VariableExample
OTEL_EXPORTER_OTLP_ENDPOINThttps://otlp-gateway-prod-us-east-0.grafana.net/otlp
OTEL_EXPORTER_OTLP_PROTOCOLgrpc or http/protobuf
OTEL_EXPORTER_OTLP_HEADERSAuthorization=Basic <base64>
OTEL_RESOURCE_ATTRIBUTESservice.name=app,service.namespace=team,deployment.environment=prod
OTEL_SERVICE_NAMEshorthand for service.name
OTEL_TRACES_SAMPLER / _ARGparentbased_traceidratio / 0.1

Troubleshooting

  • 401 from OTLP gateway → instance ID is not numeric, or API key missing publisher roles
  • 404 → endpoint URL wrong (must end with /otlp)
  • Spans missing → check OTEL_EXPORTER_OTLP_PROTOCOL matches transport (Cloud OTLP gateway = http/protobuf, Alloy local = grpc)
  • Node.js auto-instrumentation broken after bundling → bundlers like @vercel/ncc defeat the require hooks
  • Python under Gunicorn / uWSGI shows no spans → reinit OTel providers in a post-fork hook

Resources

What ships with it: 2 files

38.7 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most monitoring observability skills give in ~1.6k tokens

Counted across 530 of the 532 authors here whose files we hold, read 2026-09-06

  • Use structured JSON loggingin 40 of 530, across 36 files
  • Link every alert to a runbookin 29 of 530, across 27 files
  • Attach correlation IDs to every log linein 19 of 530, across 16 files
  • Alert on symptoms rather than causesin 19 of 530, across 17 files
  • Use OpenTelemetry for distributed tracingin 15 of 530, across 14 files
  • Alert on symptoms users feelin 15 of 530, across 13 files
  • Implement health check endpointsin 14 of 530, across 10 files
  • Inspect existing dashboards firstin 12 of 530, across 4 files
  • Build the minimum useful boardin 12 of 530, across 4 files
  • Start from operator questionsin 12 of 530, across 4 files
  • Propagate trace context across boundariesin 11 of 530, across 10 files
  • Include trace id in all log entriesin 10 of 530, across 9 files

Said here and by no other author read

  • Authenticate to the Grafana Cloud OTLP endpoint
  • Auto-instrument a Java app
  • Add Alloy as a buffering collector
  • Configure head sampling via environment variables

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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