Diagnose Deep Learning Failures Safely
Deep learning failures can invite risky, premature changes. This skill classifies concrete errors and prepares conservative guidance before any code modification.
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Review the Skillstore skill "safe-debug" from https://skillstore.io/skills/lllllllama-safe-debug.md and its manifest at https://skillstore.io/api/skills/lllllllama-safe-debug/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Using "safe-debug". A training run fails with a CUDA out-of-memory message after increasing image resolution.
Expected outcome:
Using "safe-debug". A checkpoint reports missing keys and size mismatches after changing the model variant.
Expected outcome:
Using "safe-debug". Loss becomes NaN shortly after mixed-precision training starts.
Expected outcome:
All external-command findings are false positives caused by Markdown backticks in Python strings or documentation; no commands are executed. Both traversal findings are fixed references to shared Markdown guidance, not user-controlled paths. No prompt injection, exfiltration intent, or other semantic threat was found.
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https://skillstore.io/skills/lllllllama-safe-debug/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/lllllllama-safe-debug?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/lllllllama-safe-debug?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/lllllllama-safe-debug/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/lllllllama-safe-debug.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>lllllllama. (2026). safe-debug security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lllllllama-safe-debug/audits/7@techreport{lllllllama-lllllllama-safe-debug-2026,
author = {lllllllama},
title = {safe-debug security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/lllllllama-safe-debug/audits/7},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "safe-debug security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "lllllllama"
date-released: "2026-08-08"
url: "https://skillstore.io/skills/lllllllama-safe-debug/audits/7"
identifiers:
- type: other
value: "skillstore:lllllllama-safe-debug:audit:7"
description: "Skillstore immutable audit report identifier"
Classify a traceback and identify low-risk checks before changing model code.
Separate model variant, checkpoint source, and loading strictness issues before adding compatibility logic.
Flag fixes that could change experiment meaning and require an approval-gated patch plan.
Diagnose this CUDA out-of-memory error: {{error}}. Identify the likely cause and suggest configuration-only checks. Do not modify code.Review this checkpoint loading failure: {{traceback}}. Check model variant, checkpoint source, and strictness assumptions. Propose the smallest fix without patching.Analyze this distributed training failure: {{error}}. Use this launch command: {{command}}. Separate environment, configuration, and code hypotheses. Recommend a savepoint if needed.Audit this training failure: {{traceback}}. Consider recent change {{change}} and experiment goal {{goal}}. Explain evidence, uncertainty, reproducibility impact, and a minimal approval-gated plan.Author
lllllllamaLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
656be3040aef5c047555a908cd5c695d22a4a548
Maintenance freshness
8/8/2026
Usage
6 downloads · 3 views
File structure
Analyze Deep Learning Repositories
Research repositories can hide important model, configuration, and evaluation relationships. This skill maps those relationships and reports suspicious patterns without changing project code.
Plan a Trustworthy Repository Reproduction
Repository reproduction often starts without a clear map of documented setup and experiment paths. This skill scans repository signals and recommends a minimal reproduction target.
Prepare Reproduction Environments and Assets
Research repositories often leave environment and asset requirements incomplete. This skill builds a conservative setup plan and records unresolved dependency and asset risks.
Plan Bounded Deep Learning Experiments
Exploratory training can consume budgets without producing comparable evidence. This skill ranks limited variants and records results for careful human review.
Resolve Paper Details for Reliable Reproduction
Repository documentation can omit details that are essential for reproducing deep learning results. This skill resolves narrow gaps from primary paper sources while documenting conflicts.
Run and Audit Reproduction Commands
Short reproduction runs often produce inconsistent evidence and unclear results. This skill executes a selected command and organizes outcomes, metrics, logs, and repository changes.
Debug Errors and Root Causes
by 89jobrien
Errors and incidents are hard to triage when evidence is spread across logs, stack traces, and services. This skill structures diagnosis, parses logs, identifies patterns, and guides minimal fixes.
Find Code Context Faster
by 89jobrien
Developers often lose time tracing why code exists and what depends on it. This skill prompts Claude, Codex, or Claude Code to gather related graph knowledge, callers, imports, and tests before changes.
Build Scalable PyTorch Lightning Training Workflows
by davila7
Deep learning projects become difficult to maintain when training, data, logging, and scaling logic are mixed. This skill provides structured PyTorch Lightning guidance and templates.
Debug Technical Failures Systematically
by Asmayaseen
Quick fixes often hide the real cause and create regressions. This skill structures investigation, hypothesis testing, implementation, and verification around collected evidence.
Diagnose Context Degradation in AI Agents
by ChakshuGautam
Long conversations and large prompts can make AI agents lose important information or follow conflicting context. This skill helps Claude, Codex, and Claude Code identify degradation patterns and choose mitigation strategies.
Teach Code by Building Apps
by Emz1998
Learning code can feel abstract when examples are disconnected from real projects. This skill teaches concepts while building working applications step by step.