opentelemetry
opentelemetry is an engineering AI skill with a core value of Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs.
Quick Facts
mkdir -p ./skills/opentelemetry && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/opentelemetry/SKILL.md -o ./skills/opentelemetry/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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
# 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
# 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-collecto🎯 Best For
- Claude users
- AI users
💡 Use Cases
- Using opentelemetry in daily workflow
- Automating repetitive engineering tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply opentelemetry to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install opentelemetry?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/opentelemetry/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.