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

—

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 generic and marketing-heavy: it explains concepts Claude already knows, provides no executable guidance, and completely ignores the bundled script and config that could make it useful. It has a skeletal workflow but no validation steps and no progressive disclosure into its own bundle.

Suggestions

Replace the vacuous sections (Output, Resources, Integration, Instructions) with concrete, executable guidance — e.g. a runnable optimizer/scheduler code snippet and a worked example.

Reference and wire up the existing bundle: point to scripts/analyze_model.py for analysis and assets/optimization_config.json as the parameter template, with the commands to invoke them.

Add explicit validation/verification checkpoints to the workflow (e.g. run a held-out eval, confirm loss decreases, abort+report on divergence) so model-code changes are checked before finishing.

Dimension	Reasoning	Score
Conciseness	The body is padded with vacuous filler Claude already knows — 'The skill produces structured output relevant to the task', 'empowers Claude to automatically optimize...enhancing their performance and efficiency' — matching the verbose/padded score-1 anchor rather than the mostly-efficient score-2.	1 / 3
Actionability	It describes rather than instructs ('Generates optimized code that implements the chosen strategies') with no executable code or commands, and never points to the bundled analyze_model.py or optimization_config.json that would make it actionable, matching the vague/no-concrete-guidance anchor.	1 / 3
Workflow Clarity	The four 'How It Works' phases give a sequence, but steps are vague with no validation or verification checkpoints for risky model-code changes, so it caps at the 'steps listed but validation gaps' anchor rather than reaching 3.	2 / 3
Progressive Disclosure	At over 50 lines with real bundle files present (scripts/analyze_model.py, assets/optimization_config.json), the body is a monolithic wall of generic text that never references or navigates to those resources, matching the monolithic score-1 anchor rather than a well-signaled structure.	1 / 3
	Total	5 / 12 Passed

Description

92%

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 strong: it states concrete techniques and outcomes, supplies explicit natural-language triggers, and clearly covers both what and when. Its only weakness is reliance on somewhat generic trigger terms that could conflict with broader performance skills.

Dimension	Reasoning	Score
Specificity	Names concrete techniques ('Adam, SGD, and learning rate scheduling') and concrete outcomes ('improve accuracy and reduce training time'), matching the 'lists multiple specific concrete actions' anchor rather than the narrower score-2 example.	3 / 3
Completeness	Explicitly answers both what (optimize via Adam/SGD/scheduling) and when ('Use when asked to...', 'Trigger with phrases like...'), matching the clear what-and-when anchor.	3 / 3
Trigger Term Quality	Provides natural phrases a user would actually say ('optimize deep learning model', 'improve model performance', 'speed up'), giving good coverage rather than the partial score-2 set.	3 / 3
Distinctiveness Conflict Risk	The deep-learning scope gives it a niche, but generic trigger words ('optimize', 'performance', 'speed up') could overlap with non-DL performance skills, so it sits at 'somewhat specific but could overlap' rather than the clearly-distinct score-3 anchor.	2 / 3
	Total	11 / 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: 6e3a65f

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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