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Logging Best Practices

Checked
mindrally·vlatest·testing·Apache-2.0

Ensures consistent, high-quality logging across systems following mindrally's established best practices for improved debugging and analysis.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mindrally/skills --skill logging-best-practices -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mindrally

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned mindrally/skills at 7682ca7771.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7682ca7771
Manifest
logging-best-practices/SKILL.md
Scanned
1 file, 105 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill ensures consistent, high-quality logging across applications and services by enforcing mindrally's established best practices. It focuses on structured logging using JSON format with specific fields for debugging, monitoring, and auditing purposes. The goal is to create machine-parseable logs that remain human-readable, improving overall system observability.

When to use it

  • When implementing new applications or services where consistent logging standards are required.
  • During refactoring efforts to standardize existing log formats across a codebase.
  • To improve debugging and troubleshooting capabilities by ensuring complete and structured error information is captured.
  • When integrating multiple microservices that need correlated tracing.
  • For compliance purposes, requiring detailed audit trails of system activity.

Key capabilities

  • Structured Logging: Enforces JSON formatted log output.
  • Standardized Fields: Requires consistent fields like timestamp, level, message, service, version, trace_id, span_id, request_id.
  • Log Level Guidance: Provides recommendations for appropriate use of DEBUG, INFO, WARN, ERROR, and FATAL levels.
  • Context Propagation: Promotes including request context (trace IDs, user context, business context) in log entries.
  • Security & Privacy: Emphasizes avoiding sensitive data logging and implementing access controls.
  • Performance Optimization: Suggests asynchronous logging, sampling, buffering, and monitoring.
  • Log Aggregation: Encourages centralizing logs with consistent formatting and search capabilities.

Example prompts

  • "Apply the Logging Best Practices to this new service."
  • "Ensure all log entries include a trace_id for request correlation."
  • "Review these existing log messages and suggest improvements based on mindrally's guidelines."

Tips & gotchas

  • Requires using a logging library that natively supports structured output (JSON).
  • Be mindful of sensitive data; avoid logging passwords, tokens, or PII. Mask or redact when necessary.
  • Consistent implementation across all services is crucial for effective log aggregation and analysis.