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.
34
Quality
3%
Does it follow best practices?
Impact
87%
0.95xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/data-augmentation-pipeline/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is severely underdeveloped, essentially just naming the skill without explaining what it actually does or providing useful trigger guidance. The redundant trigger terms and lack of concrete actions make it nearly useless for skill selection. It relies entirely on the skill name for identification rather than describing capabilities.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Generates synthetic training samples, applies image transformations (rotation, flipping, cropping), balances imbalanced datasets, and creates augmentation pipelines.'
Replace redundant trigger terms with natural variations users would say: 'augment data', 'expand training set', 'synthetic data generation', 'image augmentation', 'data balancing', 'oversample minority class'.
Add an explicit 'Use when...' clause describing scenarios: 'Use when the user needs to increase training data volume, apply transformations to images/text, or balance class distributions in ML datasets.'
| 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 'generate synthetic samples', 'apply transformations', or 'balance datasets'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and while it has a 'Triggers on' clause, it only repeats the skill name rather than providing meaningful trigger guidance. No 'Use when...' clause with explicit scenarios. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('data augmentation pipeline' listed twice) and overly specific/technical. Missing natural variations users might say like 'augment data', 'training data expansion', 'synthetic data', 'image augmentation', or 'oversample'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'data augmentation pipeline' is fairly specific to ML contexts and unlikely to conflict with unrelated skills. However, it could overlap with other ML-related skills since it doesn't clarify what specific augmentation tasks it handles versus other ML training skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a placeholder template with no actual instructional content. It describes what a data augmentation skill would do without providing any concrete techniques, code examples, or workflows. The entire content could be replaced with actual augmentation methods (rotation, flipping, noise injection, etc.) with executable code.
Suggestions
Add concrete, executable code examples for common augmentation techniques (e.g., image augmentation with torchvision.transforms, text augmentation with nlpaug)
Include a workflow showing how to integrate augmentation into a training pipeline with validation steps
Remove all meta-description content ('This skill provides...', 'Capabilities include...') and replace with actual augmentation patterns and code
Add specific examples for different data types (images, text, tabular) with copy-paste ready implementations
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about data augmentation. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude already understands. | 1 / 3 |
Actionability | No concrete code, commands, or specific techniques are provided. The skill describes what it does abstractly ('provides step-by-step guidance') but never actually provides any guidance, examples, or executable content. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps, no sequence, and no validation checkpoints. The content only describes capabilities without showing how to perform any data augmentation task. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no references to detailed materials, no links to examples, and no structured navigation to deeper content. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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- Reviewed
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