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.
Overall
score
23%
Does it follow best practices?
Validation for skill structure
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill early-stopping-callbackActivation
7%This description is severely underdeveloped, essentially just naming the skill without explaining its functionality or providing meaningful selection guidance. It relies entirely on the user saying the exact phrase 'early stopping callback' and offers no insight into the actual capabilities or use cases. The redundant trigger terms and missing action verbs make this description nearly useless for skill selection.
Suggestions
Add specific actions the skill performs, e.g., 'Monitors validation metrics during training, automatically stops when performance plateaus, saves best model checkpoints, configures patience and threshold parameters.'
Include a 'Use when...' clause with natural trigger scenarios: 'Use when training neural networks, preventing overfitting, setting up training callbacks, or when the user mentions validation loss monitoring or early termination.'
Expand trigger terms to include variations users naturally say: 'early stopping', 'stop training', 'overfitting prevention', 'validation patience', 'training callbacks', 'best model checkpoint'.
| 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., 'monitors validation loss', 'stops training when metrics plateau', 'prevents overfitting'). | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and the 'when' guidance is just a circular repetition of the skill name. There's no explicit 'Use when...' clause with meaningful trigger scenarios. | 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 convergence', or 'checkpoint best model'. | 1 / 3 |
Distinctiveness Conflict Risk | Being part of 'ML Training skill category' and mentioning 'early stopping' provides some domain specificity, but the lack of concrete details means it could overlap with other ML training skills that also handle callbacks or training optimization. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
7%This skill is essentially a placeholder with no substantive content. It describes what an early stopping callback skill would do without providing any actual implementation guidance, code examples, or technical details. The content fails to teach Claude anything it doesn't already know about early stopping.
Suggestions
Add executable code examples showing early stopping implementation in PyTorch and TensorFlow (e.g., `EarlyStopping` callback with patience, min_delta parameters)
Include specific parameters and their recommended values (patience=5-10, monitor='val_loss', restore_best_weights=True)
Provide a concrete workflow: 1) Define callback with parameters, 2) Add to training loop, 3) Handle checkpoint restoration
Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with actual technical content
| 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 steps, sequences, or processes are defined. The content only lists vague capabilities without any workflow for implementing early stopping in ML training. | 1 / 3 |
Progressive Disclosure | The content has some structure with clear sections, but there are no references to detailed materials, examples, or API documentation. The organization exists but leads nowhere useful. | 2 / 3 |
Total | 5 / 12 Passed |
Validation
69%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
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.