pytorch-lightning
1 finding (1 high), mainly command injection. Most recent scan 2026-10-21.
Second scanner: NVIDIA SkillSpector
SkillSpector found no issues but could not fully inspect the package, usually because SKILL.md refers to a file that is not present, so it fails closed to CAUTION instead of SAFE. Read it as "not fully checked", not as a finding. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
- Package
- 11 files, 79 KB
- SKILL.md
- 1137 words, 12+ sections
- Code
- Python (3)
- Allowed tools
ReadWriteEditBash- License
- Apache-2.0 license
- Compatibility
- Requires Python 3.10+ with lightning 2.6.6 and compatible PyTorch. CPU examples need no network after installation. CUDA/DDP/FSDP/DeepSpeed need suitable hardware; optional loggers require separate packages and online services need credentials/network.
SKILL.md covers: PyTorch Lightning · Overview · Installation · When to Use This Skill · Core Capabilities · 1. LightningModule - Model Definition · 2. Trainer - Training Automation · 3. LightningDataModule - Data Pipeline Organization · 4. Callbacks - Extensible Training Logic · 5. Logging - Experiment Tracking · 6. Distributed Training - Scale to Multiple Devices · 7. Best Practices
Findings
HIGH Python code block uses eval/exec Command Injectionbehavioral
Code block in references/lightning_module.md at line 106 contains potentially dangerous Python code.
references/lightning_module.md:106
model.eval()What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 10 KB |
references/best_practices.md | doc | 5.2 KB |
references/callbacks.md | doc | 5.4 KB |
references/data_module.md | doc | 5.3 KB |
references/distributed_training.md | doc | 7.4 KB |
references/lightning_module.md | doc | 5.6 KB |
references/logging.md | doc | 7.7 KB |
references/trainer.md | doc | 5.6 KB |
scripts/quick_trainer_setup.py | script | 14 KB |
scripts/template_datamodule.py | script | 6.7 KB |
scripts/template_lightning_module.py | script | 6.1 KB |
External hosts referenced
github.com lightning.ai arxiv.org docs.pytorch.org docs.neptune.ai doi.org export.arxiv.org www.comet.com www.deepspeed.ai
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Environment variables referenced
COMET_API_KEY MLFLOW_TRACKING_URI
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-pytorch-lightning-scan.json
- Badge for your README:
- Request a re-scan