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
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 extremely thin and template-like, providing almost no actionable information about what the skill does or when it should be selected. It merely names the skill category and repeats the same trigger term twice, offering no concrete capabilities, no natural user keywords, and no explicit usage guidance.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Applies image transformations (rotation, flipping, cropping), generates synthetic samples, and balances class distributions for ML training datasets.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about augmenting training data, expanding datasets, applying image/text transformations, or generating synthetic training samples.'
Include varied natural keywords users might say, such as 'augment data', 'synthetic data generation', 'training set expansion', 'oversampling', 'image transforms', etc.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('data augmentation pipeline', 'ML Training') but does not describe any concrete actions. There are no specific capabilities listed such as what transformations, techniques, or operations the skill performs. | 1 / 3 |
Completeness | The description fails to clearly answer 'what does this do' beyond naming the category, and there is no explicit 'when should Claude use it' clause. The 'Triggers on' line is just a duplicate of the skill name rather than meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'data augmentation pipeline' repeated twice. There are no natural keyword variations a user might say, such as 'augment training data', 'image augmentation', 'synthetic data', 'training set expansion', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'data augmentation pipeline' is somewhat specific to a niche within ML, which provides some distinctiveness. However, the lack of concrete actions or detailed scope means it could overlap with other ML-related 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 an empty shell with no actual instructional content. It consists entirely of boilerplate meta-descriptions that tell Claude what the skill supposedly does without providing any actionable guidance, code examples, or concrete techniques for data augmentation. It would provide zero value in a context window.
Suggestions
Replace the meta-description sections with actual data augmentation techniques and executable code examples (e.g., using torchvision.transforms, albumentations, or tf.image for image augmentation; nlpaug or back-translation for text augmentation).
Add a concrete workflow with steps: load dataset → select augmentation strategy → apply transforms → validate augmented samples → integrate into training pipeline, with validation checkpoints.
Remove the 'When to Use', 'Example Triggers', and 'Capabilities' sections entirely—these are YAML frontmatter concerns, not body content, and waste tokens on information Claude doesn't need.
Include at least one complete, copy-paste-ready code example showing a data augmentation pipeline (e.g., a PyTorch Dataset with augmentation transforms) with expected output format.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague idea ('data augmentation pipeline') without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific techniques, no examples of augmentation strategies. The content describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually delivering any. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. The skill claims to provide 'step-by-step guidance' but contains none. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic block of meta-descriptions with no references to detailed materials, no links to examples or advanced guides, and no meaningful structural organization beyond boilerplate headings. | 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 | |
- Commit
3076d78
- Reviewed
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