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.
npx agentmods add skills/denyszhak/pystack-skills/python-structlog-loggingnpx skills add denyszhak/pystack-skills --skill python-structlog-logginggit clone --depth 1 https://github.com/denyszhak/pystack-skillsWhat 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.
| Model | Per session | Once 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 |
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.
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.
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.
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.
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.
- yesterday First seen · 248 lines · 141 tokens per session scan A 5fa556ce69c1
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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