python-structlog-logging

A Python logging guide built around structlog, a library for writing logs as structured fields rather than plain unlabelled text. It covers JSON logs, log levels, library integration, and request correlation IDs.

In plain words
What is it for?
Setting up logging in a Python service, writing structured log calls, attaching request context, and diagnosing missing library logs.
Why use it?
It gives a service a consistent logging setup so events can be searched by fields such as user or request ID, while still handling logs from standard Python libraries.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/denyszhak/pystack-skills/python-structlog-logging
Any agent
npx skills add denyszhak/pystack-skills --skill python-structlog-logging
Clone the repo
git clone --depth 1 https://github.com/denyszhak/pystack-skills

Made for: Claude Code, Codex.

Per session 141 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,253 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00141 $0.02253
Opus 5 $0.00071 $0.01126
Sonnet 5 $0.00028 $0.00451
Haiku 4.5 $0.00014 $0.00225

Measured yesterday against content hash 5fa556ce69c1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

python-structlog-logging scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 2 executable files (examples/logging_setup.py, examples/middleware.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/python-structlog-logging/SKILL.md · 248 lines

How it starts

The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Logging with structlog

Logs are the cheapest observability. With structlog, every log line is a structured event with key=value fields — searchable in Loki, Datadog, CloudWatch without regex acrobatics. Per-request context (correlation_id, user_id) propagates automatically via contextvars, so call sites stay focused on what just happened rather than on plumbing.

This skill encodes the canonical setup: structlog primary, stdlib bridge for libraries, env-driven console renderer for local dev and JSON for everywhere else.

When to use this skill

  • Setting up logging in a new service (app/common/logging.py, middleware)
  • Writing log calls in any code (log.info("order.placed", order_id=...))
  • Adding fields to a per-request context (user_id, tenant_id)
  • Diagnosing why library logs (SQLAlchemy, httpx) aren't appearing in the structured stream

The rules

1. Configure structlog once, in configure_logging(env=...)

# app/common/logging.py — shape; full version in examples/logging_setup.py
correlation_id_var: Final[contextvars.ContextVar[str]] = contextvars.ContextVar(
    "correlation_id", default="-",
)


def configure_logging(*, env: str) -> None:
    is_local = env == "local"
    renderer = (
        structlog.dev.ConsoleRenderer(colors=True) if is_local
        else structlog.processors.JSONRenderer()
    )
    # Configure structlog with: contextvars merge, correlation_id, ISO timestamp,
    # log level, exc_info, then the renderer.
    # Bridge stdlib logging through structlog.stdlib.ProcessorFormatter so
    # library logs (SQLAlchemy, httpx, uvicorn) join the structured stream.
    ...

Called once in provide_app(config):

def provide_app(config: AppConfig) -> FastAPI:
    configure_logging(env=config.ENV)
    ...

2. correlation_id_var is a module-level ContextVar

Why ContextVar: it's the only way to propagate per-request data through async code without passing it as an arg. Async tasks inherit the contextvar from their parent task automatically.

Read the full file on GitHub · 248 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 248 lines · 141 tokens per session scan A 5fa556ce69c1

Subscribe to this mod's changes

python-structlog-logging is a skill published in the GitHub repository denyszhak/pystack-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 141 tokens to every session and 2,253 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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