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

56%

Does it follow best practices?

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Securityby

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

Quality

Content

20%

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

The body is a generic, prose-heavy skill description: no executable guidance, abstract workflow steps with no validation, and no links to the bundled scripts and assets that clearly belong in separate files. It reads as boilerplate rather than actionable instructions.

Suggestions

Replace the generic prose with concrete, executable guidance: show how to run the bundled scripts (e.g., `python scripts/analyze_model.py <model_path>`), reference assets/optimization_config.json parameters, and give a copy-paste-ready optimizer/scheduler code snippet.

Add explicit validation/verification checkpoints to the workflow (e.g., re-evaluate accuracy and training time after each change, fail fast if loss diverges) so the multi-step process has feedback loops.

Wire up progressive disclosure by linking the bundle files from the relevant sections (e.g., 'See scripts/analyze_model.py for analysis', 'Configuration: assets/optimization_config.json') instead of the generic 'Resources' placeholders, and trim the marketing/boilerplate sections to cut tokens.

Dimension	Reasoning	Score
Conciseness	The body is padded with generic marketing-style prose Claude already knows ('This skill empowers Claude to automatically optimize deep learning models... It intelligently applies various optimization techniques') plus boilerplate sections (Output, Error Handling, Resources) that add no concrete value, matching the 'verbose; padded with unnecessary context' anchor rather than mostly-efficient content.	1 / 3
Actionability	There is no executable code, no commands, and no concrete parameters; it describes rather than instructs ('Generates optimized code that implements the chosen strategies', 'Apply techniques like batch size adjustment or optimizer selection'), matching the 'vague or abstract; no concrete code/commands' anchor almost exactly.	1 / 3
Workflow Clarity	The 'How It Works' section lists a four-step sequence (Analyze, Identify, Apply, Evaluate), so a sequence is present, but the steps are abstract with no validation checkpoints; per the scoring notes, a workflow that modifies/generated code without validation checkpoints is capped at 2.	2 / 3
Progressive Disclosure	The body is organized into clear section headers, but it makes zero reference to the actual bundle files (scripts/analyze_model.py, assets/optimization_config.json, and the READMEs listing further scripts/assets), and the 'Resources' section offers only generic placeholders with no links, so structure exists but bundle navigation/signaling is missing.	2 / 3
	Total	6 / 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 capabilities, includes explicit 'Use when' trigger guidance, and uses natural phrasing a user would say. Its main weakness is that several trigger words are generic enough to risk overlap with broader performance/optimization skills.

Suggestions

Tie the bare trigger words to the deep-learning context (e.g., 'optimize deep learning model' rather than the standalone 'optimize'/'performance') to reduce conflict with general optimization skills.

Consider adding a couple of narrower trigger phrasings users say for this niche (e.g., 'tune learning rate', 'reduce training time', 'fix overfitting') to broaden natural coverage.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete optimization actions and named techniques ('Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time'), matching the anchor for listing several specific concrete actions rather than just naming a domain.	3 / 3
Completeness	It explicitly answers both 'what' (optimize models via Adam/SGD/scheduling to improve accuracy and cut training time) and 'when' via an explicit 'Use when asked to...' clause, matching the anchor requiring both with explicit triggers.	3 / 3
Trigger Term Quality	It provides several natural phrasings a user would actually say ('optimize deep learning model', 'improve model performance', 'speed up'), giving good coverage of natural trigger terms rather than only jargon.	3 / 3
Distinctiveness Conflict Risk	The deep-learning optimization niche is reasonably specific, but the bare trigger words ('optimize', 'performance', 'speed up') are generic and could overlap with general performance or optimization skills, so it sits at 'somewhat specific but could still overlap' rather than a clear, conflict-free niche.	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
Path: plugins/ai-ml/deep-learning-optimizer/skills/optimizing-deep-learning-models/SKILL.md
Commit: 15940c5

Reviewed: 2 days ago

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