Skip to content
TrustedSkillsbeta
Back to all skills

Rlm Debugging

Checked
doubleuuser·vlatest·testing·Apache-2.0

Identifies and suggests fixes for Reinforcement Learning Model training errors based on logs and metrics.

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 doubleuuser/rlm-workflow --skill rlm-debugging --full-depth -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

doubleuuser

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 doubleuuser/rlm-workflow at cafa4513fe.

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

Commit
cafa4513fe
Manifest
skills/rlm-debugging/SKILL.md
Scanned
1 file, 450 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, rlm-debugging, provides capabilities for debugging and troubleshooting workflows. It allows you to identify and resolve issues within a workflow execution. The tool is designed to aid in understanding errors and improving the overall reliability of automated processes.

When to use it

  • When an RLM (Retrieval Language Model) workflow fails or produces unexpected results.
  • During development and testing phases to pinpoint bottlenecks or logical errors.
  • To diagnose issues reported by users interacting with a complex, automated system.
  • For proactively identifying potential failure points in a workflow.

Key capabilities

  • Debugging RLM workflows
  • Troubleshooting workflow execution
  • Identifying error sources within a workflow

Example prompts

  • "Debug the last run of my inventory management workflow."
  • "What caused the 'Order Processing' step to fail?"
  • "Show me the logs for the 'Customer Onboarding' workflow from yesterday."

Tips & gotchas

This skill is most effective when used in conjunction with a well-defined RLM workflow. Ensure you have sufficient logging enabled within your workflows to provide useful debugging information.