Skills safe-debug
๐Ÿ“ฆ

safe-debug

Content revision r2 Safe โš™๏ธ External commands๐Ÿ“ Filesystem access

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.

Supports: Claude Codex Code(CC)
๐Ÿฅˆ 80 Silver

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
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.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Test it

Using "safe-debug". A training run fails with a CUDA out-of-memory message after increasing image resolution.

Expected outcome:

  • Category: CUDA out of memory.
  • Check effective batch size, input resolution, and mixed precision before changing model code.
  • Patch authorization remains false.

Using "safe-debug". A checkpoint reports missing keys and size mismatches after changing the model variant.

Expected outcome:

  • Category: checkpoint mismatch.
  • Verify the checkpoint source, selected model variant, and loading strictness.
  • Create a savepoint before any approved compatibility change.

Using "safe-debug". Loss becomes NaN shortly after mixed-precision training starts.

Expected outcome:

  • Category: NaN loss.
  • Inspect data ranges, loss inputs, mixed precision, and learning rate.
  • Use a short controlled run and require approval before patching.

Security Audit

Safe
v7 โ€ข 8/8/2026 Open versioned report

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.

4
Files scanned
238
Lines analyzed
0
Review items
0
False positives ignored
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History โ†’
Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report
Security Assessment

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https://skillstore.io/skills/lllllllama-safe-debug/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA ยท BibTeX ยท CFF)

APA citation

lllllllama. (2026). safe-debug security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lllllllama-safe-debug/audits/7

BibTeX citation

@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} }

CITATION.cff

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"

Skillstore Score

Why this score Evidence Confidence: High
64
Architecture
85
Maintainability
87
Content
67
Community
83
Spec Compliance

What You Can Build

Triage a Training Failure

Classify a traceback and identify low-risk checks before changing model code.

Review a Checkpoint Error

Separate model variant, checkpoint source, and loading strictness issues before adding compatibility logic.

Protect Experiment Comparability

Flag fixes that could change experiment meaning and require an approval-gated patch plan.

Try These Prompts

Diagnose a CUDA Error
Diagnose this CUDA out-of-memory error: {{error}}. Identify the likely cause and suggest configuration-only checks. Do not modify code.
Analyze a Checkpoint Failure
Review this checkpoint loading failure: {{traceback}}. Check model variant, checkpoint source, and strictness assumptions. Propose the smallest fix without patching.
Narrow a Distributed Failure
Analyze this distributed training failure: {{error}}. Use this launch command: {{command}}. Separate environment, configuration, and code hypotheses. Recommend a savepoint if needed.
Audit Research Impact
Audit this training failure: {{traceback}}. Consider recent change {{change}} and experiment goal {{goal}}. Explain evidence, uncertainty, reproducibility impact, and a minimal approval-gated plan.

Best Practices

  • Provide the complete traceback, launch command, and recent relevant change.
  • Review the diagnosis and approve a specific minimal fix before editing code.
  • Create a savepoint when the proposed change could affect experiment meaning.

Avoid

  • Do not use this skill for broad repository exploration or refactoring.
  • Do not treat keyword classification as proof of the root cause.
  • Do not apply suggested changes without reviewing research comparability.

Frequently Asked Questions

Does this skill change my code?
No. It diagnoses failures and prepares suggestions while keeping patch authorization false.
Which failures can it classify?
It recognizes common CUDA, checkpoint, distributed, device, shape, NaN loss, file, and runtime failure signals.
Can it inspect my repository?
No. The included script analyzes supplied error text and does not inspect source files.
What files does it create?
It can create diagnosis, patch plan, and status reports in the selected output directory.
When is a savepoint recommended?
It recommends one for checkpoint, distributed, shape, and NaN loss categories.
Can it confirm that a fix works?
No. You must apply any approved fix and verify it with an appropriate controlled run.

Developer Details

Author

lllllllama

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Ref

656be3040aef5c047555a908cd5c695d22a4a548

Maintenance freshness

8/8/2026

Usage

6 downloads ยท 3 views

File structure

๐Ÿ“ agents/

๐Ÿ“„ openai.yaml

๐Ÿ“ references/

๐Ÿ“„ debug-policy.md

๐Ÿ“ scripts/

๐Ÿ“„ safe_debug.py

๐Ÿ“„ SKILL.md