optimizing-deep-learning-models

Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to "optimize deep learning model" or "improve model performance". Trigger with phrases like 'optimize', 'performance', or 'speed up'.

Quality

38%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/ai-ml/deep-learning-optimizer/skills/optimizing-deep-learning-models/SKILL.md

Quality

Content

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is almost entirely composed of generic, abstract descriptions with no executable code, no concrete examples, and no actionable guidance. It reads like a marketing description of what a skill could do rather than actual instructions for Claude. Every section either explains things Claude already knows or describes actions in vague terms without providing the implementation details needed to actually optimize a deep learning model.

Suggestions

Replace the abstract 'Examples' section with concrete, executable code showing actual optimizer configurations (e.g., PyTorch Adam with specific hyperparameters, learning rate scheduler setup, training loop with validation).

Remove filler sections ('Overview', 'When to Use', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', 'Resources') and replace with a concise quick-start code block and specific optimization recipes.

Add concrete validation steps such as comparing training/validation loss curves, measuring wall-clock time before and after optimization, and checking for overfitting indicators.

Provide specific, copy-paste-ready code snippets for common optimization patterns (e.g., cosine annealing LR schedule, gradient clipping, mixed precision training) rather than listing technique names.

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive padding. Sections like 'Overview', 'When to Use This Skill', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' are all filler that explain nothing Claude doesn't already know. The 'How It Works' section describes abstract steps rather than providing actionable content. The entire file could be reduced to a fraction of its size.	1 / 3
Actionability	No executable code, no concrete commands, no specific examples with actual code. The 'Examples' section describes what the skill 'will do' in abstract terms rather than showing actual optimizer configurations, learning rate schedules, or PyTorch/TensorFlow code. The 'Best Practices' section lists concepts without any implementation details.	1 / 3
Workflow Clarity	The workflow steps ('Analyze Model', 'Identify Optimizations', 'Apply Optimizations', 'Evaluate Performance') are entirely abstract with no concrete actions, no validation checkpoints, and no feedback loops. There's no guidance on how to actually measure improvement or what to do if optimizations don't help.	1 / 3
Progressive Disclosure	No bundle files exist, yet the content is a monolithic wall of vague text with no meaningful structure. Sections like 'Resources' reference 'Project documentation' and 'Related skills' without any actual links. The content that is present is mostly filler rather than being well-organized actionable material.	1 / 3
	Total	4 / 12 Passed

Description

77%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description does a good job of specifying concrete techniques (Adam, SGD, learning rate scheduling) and includes explicit 'Use when' guidance with trigger phrases. However, the trigger terms are overly generic ('optimize', 'performance', 'speed up') which creates conflict risk with non-deep-learning optimization skills, and the description misses common user phrasings related to training issues.

Suggestions

Add more domain-specific trigger terms users would naturally say, such as 'learning rate', 'training loss', 'convergence', 'hyperparameter tuning', 'optimizer', 'epochs', or 'loss not decreasing'.

Make trigger phrases more distinctive by qualifying them, e.g., 'optimize neural network training' rather than just 'optimize' or 'performance', to reduce conflict with general optimization skills.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: optimizing deep learning models using Adam, SGD, and learning rate scheduling, with clear goals of improving accuracy and reducing training time.	3 / 3
Completeness	Clearly answers both 'what' (optimize deep learning models using Adam, SGD, learning rate scheduling) and 'when' (explicit 'Use when' clause and trigger phrases are provided).	3 / 3
Trigger Term Quality	Includes some relevant trigger terms like 'optimize', 'performance', 'speed up', 'deep learning model', but misses common variations users might say such as 'training loop', 'hyperparameter tuning', 'convergence', 'loss not decreasing', 'learning rate', or 'optimizer'. The listed triggers are also somewhat generic and could apply to non-deep-learning optimization.	2 / 3
Distinctiveness Conflict Risk	The trigger terms 'optimize', 'performance', and 'speed up' are quite generic and could easily conflict with skills related to code optimization, database optimization, or general performance tuning. The 'deep learning' qualifier helps but the broad triggers weaken distinctiveness.	2 / 3
	Total	10 / 12 Passed

Validation

81%

Warnings & errors only

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

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 28e6c15

Reviewed: 3 days ago

Table of Contents

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