adapting-transfer-learning-models

Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

Quality

38%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./plugins/ai-ml/transfer-learning-adapter/skills/adapting-transfer-learning-models/SKILL.md

Quality

Content

27%Weight 40%Scale 1-3

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, templated overview that describes intent rather than providing executable guidance: no code, no commands, no validation checkpoints, and no navigation to the bundled files that are meant to support it. The bundle is largely disconnected from and partly missing relative to what is referenced.

Suggestions

Add at least one complete, executable example (e.g. a PyTorch fine-tuning snippet with imports and a runnable command) instead of only describing that the skill 'will generate code'.

Insert explicit validation/feedback steps into the workflow (e.g. 'Run validate_data.py; if checks fail, fix the dataset and re-run before fine-tuning') for the destructive/batch training operations.

Either link the bundle files from the body (e.g. 'See scripts/adapt_model.py' and 'See assets/data_preprocessing_example.py') and create the missing scripts, or remove the references to non-existent scripts so navigation is one level deep and truthful.

Dimension	Reasoning	Score
Conciseness	Most sections are reasonably short, but generic placeholder sections (Instructions, Output, Error Handling, Prerequisites, Integration, Resources) add padding without skill-specific value, matching the 'mostly efficient but could be tightened' anchor; not a 1 because there is no lengthy explanation of concepts Claude already knows.	2 / 3
Actionability	The body only describes what the skill will do ('Creates Python code using appropriate ML frameworks', 'The skill will: 1. Download the ResNet50 model...') with no executable code, commands, or copy-paste-ready examples, matching the 'vague or abstract; describes rather than instructs' anchor.	1 / 3
Workflow Clarity	The 'How It Works' section lists a 5-step sequence, but the steps are abstract and lack validation checkpoints or feedback loops for a batch/training operation, matching the 'steps listed but validation gaps' anchor; not a 1 because a clear sequence is present.	2 / 3
Progressive Disclosure	Bundle directories exist (assets/ has real files; scripts/README.md lists scripts that do not exist; references/ is empty), but the body never links to or signals any of them and the referenced scripts are missing, matching the 'poor organization / references not clearly signaled / disconnected' end of the scale.	1 / 3
	Total	6 / 12 Passed

Description

50%Weight 40%Scale 1-3

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 names the right domain and several concrete actions but is undercut by templated, broken phrasing ('Build this skill automates'), a truncated sentence, and generic trigger filler instead of a real 'Use when' clause. It conveys intent but reads as an unedited auto-generated template.

Suggestions

Rewrite in clean third person, e.g. 'Adapts pre-trained ML models (ResNet, BERT, GPT) to new tasks via fine-tuning and layer freezing. Use when fine-tuning a model, adapting a pre-trained model to a new dataset, or applying transfer learning to a specific task.'

Replace the generic 'Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.' filler with concrete natural triggers a user would actually say (e.g. 'fine-tune a model', 'transfer learning', 'adapt a pre-trained model to my dataset').

Complete the truncated 'or performing...' clause or remove it so the description reads as a finished sentence.

Dimension	Reasoning	Score
Specificity	Names the transfer-learning domain and several concrete actions (adapt pre-trained models, fine-tuning, adapting to a new dataset), matching the 'names domain and some actions' anchor; it is not a 3 because the wording is templated and broken ('Build this skill automates').	2 / 3
Completeness	It states what the skill does, but the only 'when' guidance is generic template filler rather than a real explicit 'Use when...' clause, which per the guidelines caps completeness at 2.	2 / 3
Trigger Term Quality	It includes some relevant natural terms ('fine-tuning a model', 'adapting a pre-trained model to a new dataset'), but generic template filler ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.') and a truncated 'or performing...' keep it at the 'some relevant keywords but missing common variations' level.	2 / 3
Distinctiveness Conflict Risk	The transfer-learning / fine-tuning niche is reasonably distinct from most skills, but the generic trigger wording means it could still overlap with other ML skills, matching the 'somewhat specific but could overlap' anchor.	2 / 3
	Total	8 / 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/transfer-learning-adapter/skills/adapting-transfer-learning-models/SKILL.md
Commit: 0540a62

Reviewed: 1 day ago

Table of Contents

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