python-observability

Overview of python-observability skill

What python-observability skill helps you do

The python-observability skill gives an agent a practical playbook for instrumenting Python services with structured logging, metrics, and distributed tracing. It is best for teams adding production diagnostics to APIs, workers, or background jobs and for developers trying to debug incidents without guessing from incomplete logs.

Best fit users and real job-to-be-done

Use python-observability when your goal is not just “add logs,” but to make a Python system explain itself in production. The real job is to answer questions like:

• What request failed?
• Where in the request path did it fail?
• How often is it failing?
• Is latency rising before errors appear?
• Can I connect logs, metrics, and traces for one incident?

This is especially useful for backend engineers, platform teams, and AI coding agents working inside existing Python services.

What makes this skill different from a generic prompt

A generic prompt may produce ad hoc logging code. The python-observability skill is more opinionated about the parts that matter in production:

• structured JSON logs instead of free-text logs
• the four golden signals: latency, traffic, errors, saturation
• correlation IDs to connect events across request chains
• bounded metric cardinality so monitoring stays affordable and usable
• tracing as part of request-level diagnosis, not an afterthought

That combination makes it more useful for install decisions and implementation planning than a broad “monitor my app” request.

What it covers well

The current skill is strongest as a design and implementation guide for:

• structlog-style structured logging
• Prometheus-oriented metrics thinking
• tracing and correlation concepts
• production debugging patterns
• observability-first service instrumentation

It is concise on vendor-specific setup, so it works best if you already know your stack choices for telemetry export and dashboards.

Where it is lighter

Before adopting python-observability, know that it is not a full turnkey integration package. It does not appear to ship helper scripts, reference configs, or framework-specific setup files in this skill folder. Expect to supply your own runtime context, such as:

• web framework (FastAPI, Django, Flask)
• metrics backend
• tracing backend
• logging pipeline
• deployment environment

That is fine if you want guidance and code patterns, but less ideal if you want one-command setup.

How to Use python-observability skill

Install context and how to add the skill

If you are using the Skills ecosystem around the wshobson/agents repository, install from the repo and target this specific skill:

npx skills add https://github.com/wshobson/agents --skill python-observability

After install, open:

• plugins/python-development/skills/python-observability/SKILL.md

There are no extra support files surfaced for this skill, so SKILL.md is the main source of truth.

Read this file first

Start with the “When to Use This Skill” and “Core Concepts” sections in SKILL.md. That gives you the decision frame before you ask an agent to write code. The most important concepts to absorb first are:

• structured logging
• four golden signals
• correlation IDs
• bounded cardinality

If you skip those, you are likely to get instrumentation that looks complete but creates noisy logs or unusable metrics.

What input python-observability needs from you

The python-observability usage quality depends heavily on the context you provide. Give the agent:

• your Python framework and entry points
• whether the app is sync, async, or mixed
• where requests begin and end
• what background jobs or queue consumers exist
• current logging library, if any
• monitoring stack: Prometheus, OpenTelemetry, Datadog, etc.
• what incidents you want to diagnose faster
• fields that should be attached to every request
• labels that are safe and bounded for metrics

Without this, the agent can only give generic snippets.

Turn a rough goal into a strong prompt

Weak prompt:

Add observability to my Python app.

Stronger prompt:

Use the python-observability skill to instrument my FastAPI service. Add JSON structured logging, request correlation IDs, Prometheus metrics for latency, request count, error count, and saturation-related signals where feasible, plus tracing hooks. Keep metric labels bounded. Show middleware placement, example log fields, and explain what should be emitted at request start, success, and failure.

This works better because it names the framework, expected outputs, telemetry types, and key constraints.

What good python-observability usage looks like

A good result from the python-observability skill usually includes:

• a logging bootstrap section
• request or job context propagation
• correlation ID creation and propagation
• metrics defined at service boundaries
• warnings against high-cardinality labels like raw user_id
• trace/span placement around inbound requests and outbound calls
• examples of useful event fields for debugging failures

If the output is only “add a logger” or “enable Prometheus,” ask for a second pass with explicit golden-signal coverage.

Practical workflow for implementation

Use this sequence:

1. Identify one service boundary: HTTP request, queue job, CLI task.
2. Add structured logs first.
3. Add a correlation ID that appears in logs and traces.
4. Instrument the four golden signals at that boundary.
5. Add spans around critical downstream calls.
6. Review labels for cardinality risk.
7. Test failure paths, not just success paths.

This order keeps the rollout understandable and reduces the chance of shipping expensive or noisy telemetry.

Logging guidance that materially affects output quality

When using python-observability install guidance in a real codebase, ask the agent to separate local and production logging concerns. The skill explicitly favors machine-readable JSON logs in production. That matters because many teams accidentally optimize for terminal readability and later struggle with search, alerting, and correlation.

Ask for:

• stable event names
• consistent field names
• timestamps
• severity
• request identifiers
• service name
• endpoint or operation name
• error type and message on failures

Avoid asking for verbose payload dumps by default, especially if they may contain secrets or high-cardinality values.

Metrics guidance that prevents costly mistakes

The most important implementation constraint in python-observability is bounded cardinality. This is the difference between useful dashboards and runaway metrics costs.

Good metric labels:

• route template
• HTTP method
• status class or status code if controlled
• worker type
• queue name if bounded

Bad metric labels:

• user_id
• email
• request ID
• full URL with dynamic segments
• raw exception messages

If you want the agent to generate metrics code, explicitly tell it which labels are allowed.

Tracing and correlation ID usage

For tracing, the skill is most useful when you need end-to-end diagnosis across service boundaries. Ask the agent to make correlation explicit:

• where the ID is created
• how it is extracted from inbound requests
• how it flows into logs
• how it is attached to outbound requests or spans

This is often the difference between “we have logs” and “we can reconstruct one failing transaction.”

Repository-reading path for faster adoption

Because this skill folder only exposes SKILL.md, your fastest evaluation path is:

1. skim When to Use This Skill
2. read Core Concepts
3. inspect the quick-start code example
4. look for sections on logging, metrics, tracing, and debugging
5. map those patterns into your framework

Do not over-read first. The skill is compact enough that a targeted pass is better than a broad repository exploration.

python-observability skill FAQ

Is python-observability good for beginners?

Yes, if you already understand basic Python application structure. The concepts are approachable, but the best results come when you can identify request boundaries, middleware/hooks, and downstream calls in your own app. Beginners may still need framework-specific help for wiring.

Is this skill enough for production rollout by itself?

Usually not by itself. The python-observability skill gives strong conceptual and code-pattern guidance, but you will still need decisions about exporters, dashboards, alerting, storage, and framework integration details.

When is python-observability a strong fit?

It is a strong fit when you are:

• adding observability to an existing Python service
• standardizing logging across services
• instrumenting a service before launch
• debugging recurring production issues
• trying to connect logs, metrics, and traces coherently

When should I not use python-observability?

It is a weaker fit if you need:

• a vendor-specific setup wizard
• deep framework-specific integration docs only
• infrastructure monitoring outside the Python app layer
• prebuilt dashboards and alert rules bundled in the skill

In those cases, combine it with framework docs and your observability platform docs.

How is this better than an ordinary prompt?

Ordinary prompts often miss one of the critical pieces: structured logs, usable metrics, or trace correlation. python-observability improves decision quality by centering production-safe patterns like bounded cardinality and correlation IDs, which generic code generation often overlooks.

Does python-observability assume Prometheus only?

No. The skill mentions Prometheus-oriented metrics concepts, but the core value is broader: instrument the right signals with safe labels. You can adapt that to other metric backends if your team uses a different stack.

How to Improve python-observability skill

Give the agent service boundaries, not vague goals

The fastest way to improve python-observability results is to define exactly where telemetry begins and ends. Instead of saying “instrument the app,” say:

• instrument inbound HTTP requests
• instrument Celery tasks
• instrument database and external API calls
• expose metrics on /metrics

That gives the agent a concrete map for logs, counters, histograms, and spans.

Specify your allowed metric labels up front

Many weak outputs happen because the agent invents labels. Prevent that by stating:

• allowed route label format
• whether status code should be exact or grouped
• whether tenant or customer labels are forbidden
• whether job names are bounded

This directly improves the safety of the generated metrics.

Ask for event schemas, not just code snippets

If you want better operational consistency, ask the agent to define log event shapes. Example:

Using python-observability, propose 6 standard log events for request lifecycle and external API failures, with required fields and sample JSON output.

This produces more reusable observability than one-off instrumentation fragments.

Force failure-path coverage in the first pass

A common failure mode is instrumentation that only models successful requests. Ask explicitly for:

• timeout handling
• exception logging
• error counters
• latency on failed requests
• trace/span status on failure
• correlation ID presence during exceptions

That makes the output closer to production reality.

Request a review of cardinality and noise

After the first draft, ask the agent:

Review this instrumentation for high-cardinality labels, duplicated logs, missing correlation IDs, and metrics that will be hard to alert on.

This second-pass review is often more valuable than asking for more code.

Improve output by supplying real sample endpoints

If you provide concrete routes, task names, or API calls, the skill can produce better naming and metric boundaries. For example:

• GET /orders/{order_id}
• POST /checkout
• sync_inventory Celery task
• outbound call to stripe or internal inventory-service

Real examples help the agent avoid abstract instrumentation that does not match your system.

Iterate from one service to a standard

The best way to scale python-observability for Observability is to start with one service and convert the result into a repeatable standard. After a successful first rollout, ask the agent to extract:

• common logger config
• shared middleware
• standard metric names
• standard label policy
• trace propagation conventions

That turns a one-off implementation into team-wide practice.