paidf-anomalygen

paidf-anomalygen is a Fine-tuning & training data skill for AI agents, published by NVIDIA-TAO in the tao-skill-bank repository on GitHub. Skillspace rates paidf-anomalygen F (45/100) and the safety scan rated it risky; the rating is based on static analysis only.

Full PAIDF AnomalyGen pipeline — fine-tune on a new anomaly dataset, generate synthetic anomaly images (SDG), evaluate quality (nn_score), and search per-sample (guidance, crop_ratio) parameters. Three modes: full (Phase 0→7: finetune then generate), finetune_only (Phase 0→1: train only), inference_only (Phase 0, 2→7: generate from an existing checkpoint). Use when the user asks to "fine-tune AnomalyGen", "generate anomaly images", "run PAIDF SDG", "evaluate SDG output quality", "run per-sample search", or run any part of the AnomalyGen pipeline, even if they only mention one phase.

Grade
F
Quality
45 / 100
Safety
Risky
GitHub stars
67

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since.

View source on GitHubNVIDIA-TAO/tao-skill-bank

Our review

Architecture
81 / 100
Maturity
Packaged: licensed, with a version or references
Security scan
High-risk patterns
License
Apache-2.0
Adoption
Adopted by 0 other repositories, in 0 collections

What the scanner flagged

  • accesses sensitive system files
  • dynamic code execution (eval/exec)

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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