ref-logging-standards

ref-logging-standards is a skill for Claude Code, Codex from deephaven/deephaven-mcp. It costs 50 tokens per session (880 once invoked), scanned A, original, Apache-2.0.

A set of Python logging rules covering logger setup, message format, log levels, sensitive data, and required events. Logs are messages that help developers understand what a program is doing.

In plain words
What is it for?
Use it when adding or reviewing logging in Python code, especially around operations, successes, warnings, and exceptions.
Why use it?
It makes logs consistent and useful for diagnosing failures without exposing private information.

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/deephaven/deephaven-mcp/ref-logging-standards
Any agent
npx skills add deephaven/deephaven-mcp --skill ref-logging-standards
Clone the repo
git clone --depth 1 https://github.com/deephaven/deephaven-mcp

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 ref-logging-standards

README.md
[![agentmods](https://agentmods.dev/badge/skills/deephaven/deephaven-mcp/ref-logging-standards.svg)](https://agentmods.dev/skills/deephaven/deephaven-mcp/ref-logging-standards)
Your own site
<a href="https://agentmods.dev/skills/deephaven/deephaven-mcp/ref-logging-standards"><img src="https://agentmods.dev/badge/skills/deephaven/deephaven-mcp/ref-logging-standards.svg" alt="Measured on agentmods" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 880 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 $0.00050 $0.00880
Opus 5 $0.00025 $0.00440
Sonnet 5 $0.00010 $0.00176
Haiku 4.5 $0.00005 $0.00088

Measured 4d ago against content hash fbfdd65fc6a9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ref-logging-standards 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 4d 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.

.agents/skills/ref-logging-standards/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Logger Instantiation

Each module declares one private module-level logger:

_LOGGER = logging.getLogger(__name__)

Message Format

[server_or_module:function_name] Action: details

Examples:

_LOGGER.info(f"[mcp_systems_server:catalog_tables_list] Invoked: id={id!r}")
_LOGGER.info(f"[mcp_systems_server:session_enterprise_create] Success: id={id!r}")
_LOGGER.error(f"[mcp_systems_server:session_enterprise_create] Failed to create session: {e!r}", exc_info=True)

Log Levels

  • DEBUG — detailed operational steps useful for diagnosing behavior (loop iterations, intermediate values)
  • INFO — significant events: tool invocation, successful completion, notable state changes
  • WARNING — degraded but non-fatal conditions
  • ERROR — failures and exceptions; when logging a caught exception, include {e!r} in the message and exc_info=True. {e!r} is the log form only — user-facing strings render per ref-python-coding-practices rule 20; do not swap one form for the other.

When to Log

Add log statements for:

  • Entry to any significant operation (Invoked: with key parameters)
  • Successful completion (Success: or a summary of what was done)
  • Failures and exceptions (Failed to ...: {e!r})

Do not log inside tight loops or use INFO or above for routine intermediate steps — use DEBUG.

Accuracy

Log messages must accurately describe what the code does at that point. A message that says "Invoked" should appear at entry, not mid-function. An INFO message should not appear on an error path.

When refactoring (renaming a function, splitting it, moving code between modules), update the [module:function] prefix and any inline action description in every affected log line. A stale prefix is a documentation bug that survives lint and tests.

Sensitive data

Never log:

  • PSKs, auth tokens (resolved auth_token values), passwords, API keys.
  • File contents pulled in by ${file:PATH} templating (private keys, credentials).
  • Environment-variable values resolved by ${env:VAR} if the variable is known to hold a secret (*_PASSWORD, *_TOKEN, *_KEY, PSK).

Read the full file on GitHub · 78 lines

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. 4d ago First seen · 78 lines · 50 tokens per session scan A fbfdd65fc6a9

Subscribe to this mod's changes

ref-logging-standards is a skill published in the GitHub repository deephaven/deephaven-mcp (5 stars, last pushed 6d ago), licensed Apache-2.0. It adds 50 tokens to every session and 880 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens