Observability Edot Python Instrument Agent Skill
> Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/elastic/agent-skills --skill observability-edot-python-instrumentThe instruction itself
3 sections, as written by the authorEDOT Python Instrumentation
Read the setup guide before making changes:
Guidelines
- Install
elastic-opentelemetryvia pip (add torequirements.txtor equivalent) - Run
edot-bootstrap --action=installduring image build to install auto-instrumentation packages for detected
libraries
- Wrap the application entrypoint with
opentelemetry-instrument— e.g.opentelemetry-instrument gunicorn app:appor
opentelemetry-instrument python app.py. Without this, no telemetry is collected
- Set exactly three required environment variables:
OTEL_SERVICE_NAMEOTEL_EXPORTER_OTLP_ENDPOINT— must be the managed OTLP endpoint or EDOT Collector URL. Never use an APM
Server URL (no apm-server, no :8200, no /intake/v2/events)
OTEL_EXPORTER_OTLP_HEADERS—"Authorization=ApiKey <key>"or"Authorization=Bearer <token>"- Do NOT set
OTEL_TRACES_EXPORTER,OTEL_METRICS_EXPORTER, orOTEL_LOGS_EXPORTER— the defaults are already
correct
- Do NOT add code-level SDK setup (no
TracerProvider, noconfigure_azure_monitor, etc.) —
opentelemetry-instrument handles everything
- Never run both classic
elastic-apmand EDOT on the same application
Examples
See the EDOT Python setup guide for
complete examples.
Other skills for the same job
different authors, same section of the catalogue
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
Google Cloud Platform CLI - manage GCP resources including Compute Engine, Cloud Run, GKE, Cloud Functions, Storage, BigQuery, and more.
Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resilience patterns, and observability. Use PROACTIVELY when creating new backend services or APIs.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.
Microsoft Store Developer CLI (msstore) for publishing Windows applications to the Microsoft Store. Use when asked to configure Store credentials, list Store apps, check submission status, publish submissions, manage package flights, set up CI/CD for Store publishing, or integrate with Partner Center. Supports Windows App SDK/WinUI, UWP, .NET MAUI, Flutter, Electron, React Native, and PWA applications.
How to use it
Take elastic/observability-edot-python-instrument from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.