Rlm Debugging
Identifies and suggests fixes for Reinforcement Learning Model training errors based on logs and metrics.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add rlm-debugging npx -- -y @trustedskills/rlm-debugging
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"rlm-debugging": {
"command": "npx",
"args": [
"-y",
"@trustedskills/rlm-debugging"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
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.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.