How do I install the Safe Debug skill?

Run npx skillmds@latest add lllllllama/safe-debug in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Safe Debug safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Safe Debug?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Safe Debug free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Safe Debug?

lllllllama (@lllllllama) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Safe Debug by lllllllama — Agent Skill

Name: Safe Debug
Rating: 4.5 (4 reviews)
Author: lllllllama

Safe Debug

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.

lllllllama Updated Sep 9, 2026

File contents

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.

---name: safe-debugdescription: 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`.

Frequently asked questions