pytorch-patterns

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

65%

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

The body is highly actionable with complete executable code and clean internal organization, but it is monolithic (no progressive disclosure into reference files) and carries redundancy from universal Good/Bad pairing and repeated topics, with no validation-checkpointed workflows.

Suggestions

Move the longer reference material (architecture, data pipeline, performance patterns) into referenced files under references/ and keep SKILL.md as a concise overview with one-level-deep links to improve progressive disclosure.

Deduplicate topics that appear in multiple sections (mixed precision, model.eval()) so each pattern lives in one place, tightening conciseness.

Add explicit validation checkpoints to the multi-step operations, e.g. after loading a checkpoint verify shapes/keys match, to lift workflow clarity.

Dimension	Reasoning	Score
Conciseness	The body is mostly useful, executable code and avoids explaining basics Claude already knows, but the universal Good/Bad pairing for every concept plus topic duplication (mixed precision appears in both the training loop and Performance sections; model.eval() in both Validation and Anti-Patterns) means it could be tightened. It is not a 3 because not every token earns its place.	2 / 3
Actionability	The content is packed with complete, type-hinted, copy-paste-ready code — full training and validation loops, checkpoint save/load, DataLoader configs — meeting the 'fully executable code/commands; copy-paste ready' anchor.	3 / 3
Workflow Clarity	It is a well-sectioned pattern catalog with a 'When to Activate' trigger list, but the operations it describes (training, checkpointing) are presented as code rather than sequenced workflows with explicit validation checkpoints, and there are no validate→fix→retry feedback loops. It is above 1 because structure and triggers are present.	2 / 3
Progressive Disclosure	The file is internally well-organized with clear headers and a Quick Reference table, but it is a monolithic ~390-line document with no bundle files and no one-level-deep references to split detail out of SKILL.md. It is not a 1 because it is not a disorganized wall of text, and not a 3 because no content is split into referenced files.	2 / 3
	Total	9 / 12 Passed

Description

72%

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 to a clear niche and uses natural trigger terms, but it omits an explicit 'Use when...' trigger clause and leads with the abstract framing 'patterns and best practices for building', which weakens completeness and specificity.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when writing PyTorch training scripts, debugging training loops, or optimizing GPU memory.'

Replace the abstract 'patterns and best practices for building' lead with concrete verbs (e.g. 'Build, debug, and optimize PyTorch training pipelines...') to lift specificity.

Dimension	Reasoning	Score
Specificity	The description names the domain ('PyTorch deep learning') and three concrete target areas ('training pipelines, model architectures, and data loading'), but the action verb is the abstract 'building... patterns and best practices' rather than listing multiple distinct concrete actions, and 'robust, efficient, and reproducible' reads as qualifying fluff.	2 / 3
Completeness	It clearly answers 'what does this do', but there is no 'Use when...' clause or equivalent explicit trigger guidance, so per the rubric 'when' is missing and completeness is capped at 2.	2 / 3
Trigger Term Quality	It surfaces natural terms a user would actually say — 'PyTorch', 'deep learning', 'training pipelines', 'model architectures', 'data loading' — giving good coverage of the vocabulary a practitioner would use when requesting this skill.	3 / 3
Distinctiveness Conflict Risk	The 'PyTorch deep learning' niche is specific and clearly distinguishable from general-purpose or non-ML skills, making accidental triggering for an unrelated skill unlikely; it is not a level below because it is not generic.	3 / 3
	Total	10 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: affaan-m/ECC
Commit: 4130457

Reviewed: 1 day ago

Table of Contents

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