superagent: Skill for Claude Code

.agents/skills/python-observability/SKILL.md

python-observability is a skill for Claude Code, Codex from RudyCity/superagent. It costs 37 tokens per session (1,469 once invoked), scanned A, original, MIT.

A guide to adding logs, measurements, and request traces to Python applications. These records help show what happened across different parts of a running system.

In plain words
What is it for?
Use it to add structured logging, collect Prometheus metrics, trace requests across services, pass correlation IDs, and build monitoring dashboards.
Why use it?
It makes production problems easier to investigate without first changing and redeploying the code. Consistent records can show where a request failed and how the system was performing.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is RudyCity/superagent's own configuration. It tells Claude Code and Codex how to work on superagent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything superagent configures →

Reuse

Borrowing it

Nothing to install: this file belongs to RudyCity/superagent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/RudyCity/superagent/main/.agents/skills/python-observability/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/RudyCity/superagent

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for python-observability

README.md
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Your own site
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agentmods 80×15 button for python-observability

Your own site · 80×15
<a href="https://agentmods.dev/skills/rudycity/superagent/python-observability"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/python-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,469 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00037 $0.01469
Opus 5 $0.00018 $0.00734
Sonnet 5 $0.00007 $0.00294
Haiku 4.5 $0.00004 $0.00147

Measured 5d ago against content hash bf547d6ee028, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

python-observability 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 5d ago.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.agents/skills/python-observability/SKILL.md · 230 lines

How it starts

The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python Observability

Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code.

When to Use This Skill

  • Adding structured logging to applications
  • Implementing metrics collection with Prometheus
  • Setting up distributed tracing across services
  • Propagating correlation IDs through request chains
  • Debugging production issues
  • Building observability dashboards

Core Concepts

1. Structured Logging

Emit logs as JSON with consistent fields for production environments. Machine-readable logs enable powerful queries and alerts. For local development, consider human-readable formats.

2. The Four Golden Signals

Track latency, traffic, errors, and saturation for every service boundary.

3. Correlation IDs

Thread a unique ID through all logs and spans for a single request, enabling end-to-end tracing.

4. Bounded Cardinality

Keep metric label values bounded. Unbounded labels (like user IDs) explode storage costs.

Quick Start

import structlog

structlog.configure(
    processors=[
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer(),
    ],
)

logger = structlog.get_logger()
logger.info("Request processed", user_id="123", duration_ms=45)

Fundamental Patterns

Pattern 1: Structured Logging with Structlog

Configure structlog for JSON output with consistent fields.

import logging
import structlog

def configure_logging(log_level: str = "INFO") -> None:
    """Configure structured logging for the application."""
    structlog.configure(
        processors=[
            structlog.contextvars.merge_contextvars,
            structlog.processors.add_log_level,
            structlog.processors.TimeStamper(fmt="iso"),
            structlog.processors.StackInfoRenderer(),
            structlog.processors.format_exc_info,
            structlog.processors.JSONRenderer(),
        ],
        wrapper_class=structlog.make_filtering_bound_logger(
            getattr(logging, log_level.upper())
        ),
        context_class=dict,
        logger_factory=structlog.PrintLoggerFactory(),
        cache_logger_on_first_use=True,
    )

# Initialize at application startup
configure_logging("INFO")
logger = structlog.get_logger()

Read the full file on GitHub · 230 lines

Files

What ships with it

1 file 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. 5d ago First seen · 230 lines · 37 tokens per session scan A bf547d6ee028

Subscribe to this mod's changes

python-observability is a skill published in the GitHub repository RudyCity/superagent (21 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,469 once invoked, about $0.0002 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 2026-09-03.

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