data-augmentation-pipeline

Data Augmentation Pipeline - Auto-activating skill for ML Training. Triggers on: data augmentation pipeline, data augmentation pipeline Part of the ML Training skill category.

0.95x

Quality

Does it follow best practices?

Impact

87%

0.95x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/data-augmentation-pipeline/SKILL.md

Evaluation results

75%

-13%

Image Dataset Augmentation for a Retail Product Classifier

Production-ready image augmentation pipeline

Criteria

Without context

With context

Uses ML ecosystem library

100%

Train-only augmentation

100%

Output validation present

16%

Class folder structure preserved

100%

Configurable parameters

80%

90%

No hardcoded paths

100%

Summary report produced

100%

Step-by-step structure

100%

No test/val contamination in output

100%

README explains usage

100%

Without context: $0.6216 · 3m 51s · 35 turns · 36 in / 8,642 out tokens

With context: $0.7953 · 2m 52s · 41 turns · 132 in / 10,248 out tokens

100%

Time Series Anomaly Detection: Augmentation and Training Pipeline

End-to-end pipeline with experiment tracking

Criteria

Without context

With context

Experiment logging present

100%

Augmentation train-only

100%

Full pipeline scope

100%

Uses ML ecosystem library

100%

Hyperparameters in config file

100%

Random seed set

100%

Output validation

70%

100%

Step-by-step structure

100%

Summary printed

100%

No external data required

100%

Without context: $0.4252 · 1m 43s · 25 turns · 25 in / 6,266 out tokens

With context: $0.3585 · 1m 29s · 23 turns · 68 in / 5,330 out tokens

88%

Tabular Data Augmentation for a Credit Scoring Model

Tabular augmentation with output validation

Criteria

Without context

With context

Uses ML ecosystem library

100%

Output validation with stats

100%

Step-by-step documentation

100%

Train-only augmentation

Augmented CSV produced

100%

Class balance addressed

100%

Technique justified

100%

Random seed set

100%

No external data download

100%

Production-ready structure

100%

Without context: $0.5304 · 2m 20s · 27 turns · 29 in / 7,663 out tokens

With context: $0.6380 · 2m 43s · 32 turns · 349 in / 8,970 out tokens

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 994edc4

Evaluated: 17 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Image Dataset Augmentation for a Retail Product Classifier Time Series Anomaly Detection: Augmentation and Training Pipeline Tabular Data Augmentation for a Credit Scoring Model

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