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Training Log Analyzer

Checked
microck·vlatest·ai-ml

Analyzes training logs to identify patterns and areas for improvement, boosting performance and efficiency through data-driven insights.

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 microck/ordinary-claude-skills --skill training-log-analyzer -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

microck

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

    Only contacts known or declared hosts: github.com.

    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.

    Hosts contacted

    github.comsource host

  • 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 microck/ordinary-claude-skills at 1056d293d4.

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

Commit
1056d293d4
Manifest
skills_all/training-log-analyzer/SKILL.md
Scanned
2 files, 72 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

The microck-training-log-analyzer skill analyzes training logs generated by machine learning models. It can extract key metrics and insights from these logs, helping users understand model performance and identify areas for improvement. This includes identifying trends in loss, accuracy, and other relevant parameters over time.

When to use it

  • Debugging Model Training: Use this skill when your model's training isn’t progressing as expected and you need to pinpoint the cause by examining log data.
  • Performance Monitoring: Regularly analyze training logs to track a model's performance and identify potential degradation over time.
  • Hyperparameter Tuning: Gain insights into how different hyperparameter settings affect training behavior, allowing for more informed adjustments.
  • Identifying Training Issues: Quickly detect anomalies or errors in the training process that might not be immediately obvious.

Key capabilities

  • Log parsing and analysis
  • Metric extraction (loss, accuracy, etc.)
  • Trend identification over time
  • Anomaly detection

Example prompts

  • "Analyze this training log file and tell me about the trend in loss."
  • "What was the highest accuracy achieved during this training run?"
  • "Can you identify any anomalies or unusual patterns in this training log data?"

Tips & gotchas

The skill requires a properly formatted training log file as input. The specific format supported will depend on the model and logging framework used to generate the logs.