Python Logging
CheckedAutomates Python logging configuration and management within backend systems using structured data and best practices.
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python-logging. SKILL.md is marked internal, so the skills CLI hides it.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-20. We don't show a command we know will fail.
What we know about this skill
Publisher
narumiruna
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
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
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No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned narumiruna/agent-skills at 519bdaa68d.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 519bdaa68d
- Scanned
- 3 files, 128 lines
About this skill
What it does
This skill provides Python logging capabilities to AI agents. It allows agents to record events, errors, and debugging information within a Python application. This enables better monitoring, troubleshooting, and understanding of agent behavior during execution.
When to use it
- Debugging complex workflows: Use this when an agent is experiencing unexpected behavior and you need detailed logs to pinpoint the cause.
- Monitoring long-running tasks: Track progress and identify potential issues in agents performing lengthy operations.
- Auditing user interactions: Log user inputs and agent responses for compliance or analysis purposes.
- Analyzing performance bottlenecks: Identify areas where an agent's execution is slow by logging timestamps of key events.
Key capabilities
- Configurable logging levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
- Customizable log message formatting
- Output to console and/or files
- Timestamped log entries
Example prompts
- "Log a debug message indicating the current user's input."
- "Record an error with traceback information when a network request fails."
- "Create a log file named 'agent_activity.log' to store all agent actions."
Tips & gotchas
- Ensure the Python environment has the necessary logging libraries installed.
- Be mindful of the volume of logs generated, as excessive logging can impact performance and storage space.