early-stopping-callback
Early Stopping Callback - Auto-activating skill for ML Training. Triggers on: early stopping callback, early stopping callback Part of the ML Training skill category.
39
Quality
7%
Does it follow best practices?
Impact
99%
1.01xAverage 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/early-stopping-callback/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 essentially a placeholder that names the skill but provides no substantive information about what it does or when to use it. The redundant trigger terms and lack of concrete actions make it nearly useless for skill selection. It relies entirely on the user saying the exact phrase 'early stopping callback' rather than describing the problem it solves.
Suggestions
Add concrete actions describing what the skill does, e.g., 'Implements early stopping callbacks to halt model training when validation metrics stop improving, preventing overfitting and saving compute resources.'
Include a 'Use when...' clause with natural trigger terms like 'Use when implementing training callbacks, preventing overfitting, monitoring validation loss, or setting up patience-based training termination.'
Add varied trigger terms users might naturally say: 'overfitting prevention', 'training patience', 'validation monitoring', 'stop training', 'Keras/PyTorch callbacks'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the concept 'Early Stopping Callback' without describing any concrete actions. It doesn't explain what the skill actually does (e.g., 'implements early stopping to prevent overfitting', 'monitors validation loss and halts training'). | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and the 'when' clause is just a repetition of the skill name rather than meaningful trigger guidance. There's no explicit 'Use when...' clause with actionable context. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('early stopping callback' listed twice) and miss natural variations users might say like 'stop training early', 'prevent overfitting', 'validation patience', 'training callbacks', or 'halt training'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'early stopping callback' is fairly specific to ML training contexts and unlikely to conflict with unrelated skills, but within ML Training skills it could overlap with other callback-related skills without clearer differentiation of its specific purpose. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a placeholder with no actionable content. It describes what early stopping callbacks are used for in abstract terms but provides zero implementation details, code examples, or concrete guidance. The entire content could be replaced with a single code snippet showing PyTorch/TensorFlow early stopping implementation.
Suggestions
Add executable code examples for early stopping in PyTorch (EarlyStopping class) and TensorFlow (tf.keras.callbacks.EarlyStopping) with common parameters
Include concrete guidance on choosing patience values, monitoring metrics (val_loss vs val_accuracy), and restore_best_weights behavior
Remove all generic boilerplate sections (Purpose, When to Use, Capabilities, Example Triggers) and replace with actual implementation patterns
Add a workflow showing: train loop -> monitor metric -> check patience -> save/restore best weights -> stop or continue
| 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 technical content. | 1 / 3 |
Actionability | No concrete code, commands, or executable guidance is provided. The skill describes what it does in abstract terms but never shows how to actually implement an early stopping callback. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The content only lists vague capabilities without any sequence of actions or validation checkpoints for implementing early stopping. | 1 / 3 |
Progressive Disclosure | The content is organized into sections with headers, but there's no actual content to disclose. References to related skills exist but no links to detailed implementation guides or examples. | 2 / 3 |
Total | 5 / 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
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- Reviewed
Table of Contents
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