learning-rate-scheduler
Learning Rate Scheduler - Auto-activating skill for ML Training. Triggers on: learning rate scheduler, learning rate scheduler Part of the ML Training skill category.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill learning-rate-schedulerOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, essentially just restating the skill name 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 be nearly useless for Claude to distinguish this skill from others in a large skill library.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Configures learning rate schedules including step decay, cosine annealing, warmup, and custom schedulers for neural network training.'
Include a 'Use when...' clause with natural trigger terms: 'Use when the user mentions learning rate, lr schedule, warmup, decay rate, cosine annealing, or training optimization.'
Remove the duplicate trigger term and expand to include variations users actually say: 'lr', 'learning rate decay', 'scheduler', 'warmup schedule', 'step lr', etc.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('Learning Rate Scheduler', 'ML Training') but provides no concrete actions. It doesn't explain what the skill actually does - no verbs describing capabilities like 'configure', 'adjust', 'optimize', or 'schedule'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming the topic, and the 'when' clause is just a repetition of the skill name rather than meaningful trigger guidance. No explicit 'Use when...' clause with actionable triggers. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('learning rate scheduler' listed twice) and miss common variations users might say like 'lr schedule', 'warmup', 'decay', 'step scheduler', 'cosine annealing', or 'learning rate decay'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'learning rate scheduler' is a specific ML concept that provides some distinctiveness, the lack of detail about what operations it performs could cause overlap with other ML training skills that might also touch on learning rates. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill is an empty template with no actual content about learning rate schedulers. It contains only meta-descriptions of what a skill should do without any concrete guidance, code examples, or actionable information. The entire content could be replaced with actual PyTorch/TensorFlow scheduler examples and best practices.
Suggestions
Add concrete code examples for common schedulers (StepLR, CosineAnnealingLR, OneCycleLR) with executable Python snippets
Include a decision guide for when to use different scheduler types based on training scenarios
Provide specific hyperparameter recommendations (e.g., warmup steps, decay rates) with example configurations
Remove all meta-content about 'when to use' and 'capabilities' - replace with actual technical guidance
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler with no actual technical substance. It explains what the skill does in abstract terms without providing any concrete information about learning rate schedulers that Claude doesn't already know. | 1 / 3 |
Actionability | No executable code, no specific commands, no concrete examples of learning rate scheduler implementations. The content only describes what it claims to do without actually doing it. | 1 / 3 |
Workflow Clarity | No workflow is defined. Claims to provide 'step-by-step guidance' but includes zero actual steps. No validation checkpoints or process sequences are present. | 1 / 3 |
Progressive Disclosure | No references to detailed documentation, no links to examples or advanced content. The skill is a shallow placeholder with no structure pointing to deeper resources. | 1 / 3 |
Total | 4 / 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.