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

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

12%

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

The body is largely boilerplate: it describes an optimization process abstractly without executable code, validation steps, or links to the bundled scripts and config that actually exist. It reads as a template skeleton rather than actionable guidance.

Suggestions

Replace the abstract 'How It Works' prose with a concrete worked example: an actual optimizer/LR-scheduler code snippet Claude can adapt.

Link the existing bundle files in the body (e.g., 'See scripts/analyze_model.py for model analysis; assets/optimization_config.json for parameter templates').

Add explicit validation checkpoints (e.g., compare validation loss before/after, abort if accuracy drops) and remove generic template sections like 'Output', 'Resources', and 'Integration' that add no information.

Dimension	Reasoning	Score
Conciseness	The body is padded with generic template prose ("This skill empowers Claude to automatically optimize...", "The skill produces structured output relevant to the task") and restates well-known DL concepts (Adam vs SGD, L1/L2), adding little Claude does not already know.	1 / 3
Actionability	There is no executable code, no concrete commands, and no specific API usage; guidance like "apply techniques like batch size adjustment or optimizer selection" describes rather than instructs.	1 / 3
Workflow Clarity	A four-step sequence (Analyze, Identify, Apply, Evaluate) is present, but steps are abstract with no validation checkpoints or error-recovery loops for a model-modification process.	2 / 3
Progressive Disclosure	Bundle files exist (scripts/analyze_model.py, assets/optimization_config.json) but the body never references or links to any of them, leaving a monolithic wall of generic sections with no navigation to the available resources.	1 / 3
	Total	5 / 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 is specific and complete, clearly stating both capability and explicit trigger guidance. Its main weakness is generic trigger terms that raise conflict risk with other performance-oriented skills.

Suggestions

Tighten trigger terms to deep-learning-specific phrasings (e.g., 'training loss', 'convergence', 'model accuracy') instead of the generic 'performance'/'speed up'.

Consider adding a short anti-trigger note clarifying this is for neural network / deep learning models, not general code performance.

Dimension	Reasoning	Score
Specificity	Names multiple concrete techniques — "using Adam, SGD, and learning rate scheduling" and concrete goals ("improve accuracy and reduce training time"), matching the anchor for listing several specific concrete actions.	3 / 3
Completeness	Explicitly answers both what (optimize DL models via Adam/SGD/LR scheduling) and when ("Use when asked to 'optimize deep learning model' or 'improve model performance'. Trigger with phrases like...").	3 / 3
Trigger Term Quality	Includes relevant natural phrases ("optimize", "improve model performance", "speed up"), but terms like "performance" and "optimize" are generic and overlap with non-deep-learning skills, so coverage is not comprehensive for the niche.	2 / 3
Distinctiveness Conflict Risk	The deep-learning subject is a clear niche, but the trigger words "optimize", "performance", and "speed up" are broad and could fire for general performance or code-optimization tasks.	2 / 3
	Total	10 / 12 Passed

Validation

87%

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 — 14 / 16 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	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 2cafb23

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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