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.

Install with Tessl CLI

npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill data-augmentation-pipeline

What are skills?

Overall
score

19%

Review — 4%

Does it follow best practices?

Validation — 11 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

Activation

This description is severely underdeveloped, essentially just restating its title without explaining capabilities or providing useful trigger guidance. It lacks any concrete actions, has redundant trigger terms, and provides no meaningful 'Use when' clause. The description would fail to help Claude distinguish this skill from other ML-related skills.

Suggestions

Add specific concrete actions like 'Applies image transformations (rotation, flipping, cropping), generates synthetic samples, balances class distributions, and creates augmented training datasets'

Add a proper 'Use when...' clause with natural trigger terms: 'Use when the user mentions augmenting training data, expanding datasets, image transformations, synthetic data generation, or improving model generalization'

Include common variations users would say: 'augment data', 'expand dataset', 'training data preprocessing', 'image augmentation', 'text augmentation', 'oversampling'

Dimension	Reasoning	Score
Specificity	The description only names the domain 'Data Augmentation Pipeline' and category 'ML Training' but provides no concrete actions. There are no specific capabilities listed like 'applies transformations', 'generates synthetic samples', or 'augments images/text'.	1 / 3
Completeness	The description fails to answer 'what does this do' beyond naming itself, and the 'when' clause is just a duplicate trigger phrase rather than meaningful guidance. No explicit 'Use when...' clause with actionable triggers.	1 / 3
Trigger Term Quality	The trigger terms are redundant ('data augmentation pipeline' listed twice) and overly specific. Missing natural variations users would say like 'augment data', 'training data expansion', 'image augmentation', 'synthetic data', or 'data preprocessing'.	1 / 3
Distinctiveness Conflict Risk	The term 'data augmentation pipeline' is somewhat specific to ML workflows and unlikely to conflict with unrelated skills, but could overlap with general 'ML Training' or 'data preprocessing' skills without clearer boundaries.	2 / 3
	Total	5 / 12 Passed

Implementation

This skill is an empty template with no actual content about data augmentation. It contains only meta-descriptions of what a skill should do without any concrete techniques, code, or guidance for implementing augmentation pipelines. The content would be identical if you replaced 'data augmentation pipeline' with any other topic.

Suggestions

Add concrete code examples for common augmentation techniques (e.g., image augmentation with albumentations/torchvision, text augmentation with nlpaug)

Define a clear workflow: load data -> select augmentation strategy -> configure transforms -> validate augmented samples -> integrate into training pipeline

Include specific library recommendations with executable code snippets (e.g., `transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ColorJitter()])`)

Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with actual augmentation patterns and anti-patterns

Dimension	Reasoning	Score
Conciseness	The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information about data augmentation.	1 / 3
Actionability	Contains zero concrete guidance - no code examples, no specific augmentation techniques, no library recommendations, no commands. The entire content describes what the skill supposedly does rather than instructing how to do anything.	1 / 3
Workflow Clarity	No workflow is defined at all. Claims to provide 'step-by-step guidance' but contains no actual steps. A data augmentation pipeline would need clear sequences for image/text/audio augmentation, but none are present.	1 / 3
Progressive Disclosure	No structure beyond generic headings. No references to detailed documentation, no examples file, no API reference. The content is a shallow placeholder with nothing to progressively disclose.	1 / 3
	Total	4 / 12 Passed

Validation

69%

Warnings & errors only

Validation — 11 / 16 Passed

Validation for skill structure

Criteria	Description	Result
description_trigger_hint	Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...')	Warning
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
metadata_version	'metadata' field is not a dictionary	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
body_steps	No step-by-step structure detected (no ordered list); consider adding a simple workflow	Warning

	Total	11 / 16 Passed

Reviewed

22 days ago

Table of Contents

Activation Implementation Validation

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