🎯

safe-debug

🎯Skill

A skill that finds the cause of errors in deep learning research code, such as running out of GPU memory, before changing anything. It suggests the smallest fix and waits for your approval.

lllllllama/rigorpilot-skills356,760 InstallsAdded May 26, 2026

Installation

$npx skills add lllllllama/rigorpilot-skills --skill safe-debug

Run it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).

Uses skills.sh Β· Node.js 22.20+

After installing

Ask for related work and the agent uses it on its own, or call it yourself.

Run it (Claude Code)/safe-debug
Install by hand

Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.

Open the skill folder β†’
  • This project .claude/skills/safe-debug/
  • All projects ~/.claude/skills/safe-debug/

These paths are for Claude Code. For other tools, the command above puts it in the right place.

What this skill does

Overview

safe-debug is the Rigor Debug / Rigor Audit skill. You paste an error, and it narrows down the root cause before any code is touched. It keeps bug fixes separate from research contributions, so a fix is not mistaken for a research result.

Key Features

  • Paste a traceback (the error report a program prints), a terminal error, or a training failure and get a diagnosis.
  • Get help with CUDA OOM (running out of GPU memory), checkpoint load failures (a saved model that will not load), shape mismatches (data of the wrong size), and NaN loss (the training score becoming an invalid number).
  • Keep your code safe, because the skill does not change repository code by default.
  • Review the smallest possible fix and approve it before anything is patched.
  • Read the results in debug_outputs/DIAGNOSIS.md, debug_outputs/PATCH_PLAN.md, and debug_outputs/status.json.

Who is this for?

Use it when a training or inference run (using a trained model) fails and you want a careful diagnosis instead of a quick rewrite. It also helps when you need to know whether a fix changes what your experiment means or how fairly it compares to other results.

Summarized by AI from this skill's SKILL.md. Suggest a better one

Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.

safe-debug

Use this as the Rigor Debug / Rigor Audit skill. The installed slug remains safe-debug for compatibility.

Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md; this skill should guide conservative diagnosis without blocking the model from finding the local root cause.

When to apply

  • The user provides a traceback, terminal error, or concrete training or inference failure symptom.
  • The user wants diagnosis, root-cause narrowing, and minimal patch suggestions before code is changed.
  • The user wants a safe debug flow with explicit human approval before mutation.

When not to apply

  • When the user wants a broad repository walkthrough without an active failure.
  • When the task is speculative experimentation or code adaptation.
  • When the user is asking for a large refactor or readability rewrite.

Clear boundaries

  • Diagnose first.
  • Do not modify repository code by default.
  • If a patch is needed, propose the smallest fix and require explicit approval first.
  • Escalate savepoint or branch creation before medium-risk or high-risk changes.
  • A debug fix is not automatically a research contribution; if it changes experiment meaning or comparability, say so explicitly.

Output expectations

  • debug_outputs/DIAGNOSIS.md
  • debug_outputs/PATCH_PLAN.md
  • debug_outputs/status.json

Notes

Use references/debug-policy.md, ../ai-research-reproduction/references/research-rigor-principles.md, and the shared ../ai-research-reproduction/references/research-pitfall-checklist.md.

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