Guide · Agent skill
How to use the Python Observability agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The Python Observability agent skill gives an AI coding agent established patterns for making a Python application easier to monitor and debug. It covers structured JSON logging with the structlog library, correlation IDs that follow a request across services, sensible log levels, and bounded metric labels. This guide explains what the skill does, how to install it, how to ask for it in plain language, and what to check before using its code.
Python Observabilitywshobson/agents · MIT Download the skill Quick start
- 1Download and unzip the skill. Download the ZIP from this page and unzip it so the python-observability folder is available.
- 2Choose where to install it. Place the folder in ~/.claude/skills/ for personal use, in your project's .claude/skills/ for one project, or upload it under Settings → Capabilities → Skills in the Claude apps.
- 3Check your project stack. Confirm you are using Python, and install structlog, plus FastAPI and httpx if you want the web and HTTP examples.
- 4Describe the task in plain language. Ask for logging, metrics, or tracing in your own words, naming your framework and the service involved.
- 5Review the generated code. Check log fields for sensitive data such as user_id, confirm metric labels are bounded, and verify log levels.
- 6Test in your environment. Run the code and confirm the JSON logs, correlation IDs and metrics appear as expected before relying on them in production.
What is an agent skill, and what does this one make the agent do?
An agent skill is a folder of instructions and reference files that an AI coding agent can load when a task matches it. This one is named python-observability and is described as Python observability patterns including structured logging, metrics, and distributed tracing.
When it is installed, the agent knows how to write code that emits JSON logs with consistent fields, attaches a correlation ID to every log line and outgoing request so one user action can be traced across services, and uses sensible log levels and bounded metric labels. It also has Prometheus-style metric examples such as request latency histograms, request counters, error counters and a database connection pool gauge.
The skill is guidance and code templates for the agent to apply to your project. It does not monitor anything by itself and does not deploy dashboards or servers.
How do you install the Python Observability skill?
Download the ZIP file from this page and unzip it. You can then put the skill folder in one of two places: your personal skills folder at ~/.claude/skills/, or a project's .claude/skills/ folder if you want the skill available only in that project.
If you use the Claude apps instead, you can upload the skill under Settings → Capabilities → Skills.
- Personal install: unzip into ~/.claude/skills/
- Project install: unzip into the project's .claude/skills/ folder
- Claude apps: upload under Settings → Capabilities → Skills
What do you need before you start?
You need a Python project. The examples are written for Python and use specific libraries, so the skill is not useful for non-Python projects.
The skill's examples use the structlog library. If you follow the web and HTTP examples, you also need FastAPI and httpx. For metrics, the skill references Prometheus, but it does not include a specific Prometheus setup.
- Python
- structlog (used in the examples)
- FastAPI and httpx, if you follow the web and HTTP examples
- Prometheus is referenced for metrics, but no setup is included in the skill files
How do you trigger the skill with a natural request?
You do not need a special command. Describe the task in plain language that matches the skill's purpose, such as adding logging, implementing metrics collection, setting up tracing, or debugging production systems. The skill's description tells the agent when to use it.
Name your framework and what you want to end up with, so the agent knows which examples apply. Mentioning a specific endpoint or service also helps.
- Add structured JSON logging to this FastAPI app using structlog.
- Add a correlation ID to every log line and to outgoing httpx calls to the payments service.
- Set up Prometheus metrics for request latency, traffic, and errors in this service.
- Our checkout is failing intermittently in production. Help me trace one request across the services and add logging where it is missing.
What does the skill produce? Examples from the source
The skill's quick start configures structlog to render logs as JSON with an ISO timestamp. A fuller pattern adds log level, stack information and exception formatting, and turns on context variables so values bound earlier appear on later log lines.
For correlation IDs, a FastAPI middleware reads an incoming X-Correlation-ID header or generates a new UUID, then returns the same header in the response. Outgoing httpx calls pass the ID on in the same header.
For the Golden Signals, the reference file adds Prometheus histograms, counters and a gauge for latency, traffic, errors and saturation.
- Log level guidance: DEBUG for development diagnostics, INFO for normal operations, WARNING for recoverable anomalies, ERROR for failures needing attention
- Never log expected behaviour at ERROR; for example, a wrong password is INFO
- Keep metric label values bounded; unbounded labels such as user IDs can explode storage costs
logger.info("Order created", order_id=order.id, total=order.total)What should you check before using the generated code?
Review the code against your project. The examples log user_id fields, and the skill does not discuss privacy or redaction. If your project must not log user identifiers, remove or redact those fields before shipping.
The skill also does not run scripts and does not use the network, according to its listing. Its files are instructions and code templates, so you still need to test the logging and metrics in your own environment.
The skill's best practices include testing your observability in integration tests and setting up alerts, but the skill itself does not provide alerting.
- Remove or redact user_id and other sensitive fields if your policy forbids them in logs
- Check that the metric labels you add are bounded
- Confirm the libraries and versions in your project match the examples
What people use it for
- Adding JSON structured logging with consistent fields to a FastAPI service
- Propagating a correlation ID through incoming requests and outgoing httpx calls to other services
- Adding Prometheus-style latency, traffic, error and saturation metrics to an API
- Choosing log levels for an existing Python application so routine events stop appearing as errors
- Helping debug a production problem by adding the logging needed to trace one request
Frequently asked questions
Is the Python Observability skill free?
The skill is published under the MIT licence, as listed on this page. Check the licence file in the source repository for the full terms.
What do I need to use the python-observability skill?
You need a Python project. The examples use structlog, and the web and HTTP examples use FastAPI and httpx. Prometheus is referenced for metrics, but the skill does not include a Prometheus setup.
Where do I install an agent skill?
Unzip the downloaded ZIP into ~/.claude/skills/ for personal use or into a project's .claude/skills/ folder. In the Claude apps, you can upload it under Settings → Capabilities → Skills.
Does this skill set up dashboards or monitoring servers?
No. The skill provides guidance and code templates for the agent to apply to your project. It does not deploy dashboards, servers, or a monitoring stack.
Does the skill run scripts or use the network?
According to its listing, it does not run scripts and does not use the network. It consists of instructions and reference code.
Can I use this skill for a non-Python project?
It is not a good fit. The examples are specific to Python libraries such as structlog and httpx, so a project in another language will need different patterns.
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Our verdict
Use this skill if you are a backend developer adding structured logs, correlation IDs or first-time Prometheus-style metrics to a Python web service, or if your team needs request tracing across services. Pick something else if you want a finished monitoring stack, a non-Python project, or a solution that handles privacy and redaction for user identifiers.
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