adapting-transfer-learning-models
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill adapting-transfer-learning-modelsBuild 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.
Validation
81%| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 13 / 16 Passed | |
Implementation
13%This skill is largely template boilerplate with minimal actionable content. It describes what the skill does conceptually but provides no executable code, specific commands, or concrete implementation guidance. The content is padded with generic sections that apply to any skill and explains concepts Claude already understands.
Suggestions
Replace the abstract examples with actual executable Python code showing how to fine-tune ResNet50 or BERT, including specific library imports and function calls
Remove generic boilerplate sections (Prerequisites, Instructions, Output, Error Handling, Resources) that provide no skill-specific value
Add concrete validation steps with actual commands, e.g., 'Verify GPU availability: torch.cuda.is_available()' and 'Check dataset shape matches model input requirements'
Consolidate the redundant overview content and focus on a quick-start code snippet that demonstrates the core functionality
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with redundant explanations (overview repeated twice), generic boilerplate sections ('Prerequisites', 'Instructions', 'Output', 'Error Handling') that add no value, and explains concepts Claude already knows like what transfer learning is and basic ML concepts. | 1 / 3 |
Actionability | No executable code anywhere despite being a code-generation skill. Examples describe what 'the skill will do' abstractly rather than providing actual implementation code, commands, or copy-paste ready snippets. | 1 / 3 |
Workflow Clarity | The 'How It Works' section lists 5 steps in sequence, but lacks validation checkpoints, error recovery feedback loops, and concrete verification steps. The workflow is descriptive rather than prescriptive. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files. Contains generic placeholder sections that bloat the document. No clear navigation structure or separation of quick-start vs advanced content. | 1 / 3 |
Total | 5 / 12 Passed |
Activation
33%This description starts with relevant domain terminology but is severely undermined by truncated content ('performing...') and completely generic placeholder text for the trigger guidance. The 'Use when appropriate context detected' and 'Trigger with relevant phrases based on skill purpose' provide zero actionable information for skill selection, making this description largely non-functional despite having some domain-specific terms.
Suggestions
Replace the placeholder 'Use when appropriate context detected' with specific triggers like 'Use when user mentions fine-tuning, transfer learning, adapting pretrained models, domain adaptation, or training on small datasets'
Complete the truncated description to list all concrete actions (e.g., 'performing feature extraction, freezing layers, learning rate scheduling for fine-tuning')
Remove the meta-instruction 'Trigger with relevant phrases based on skill purpose' and replace with actual natural language phrases users would say
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (transfer learning, machine learning models) and some actions (fine-tuning, adapting pre-trained models), but the description is truncated ('performing...') and lacks comprehensive concrete actions like specific techniques or outputs. | 2 / 3 |
Completeness | The 'what' is partially present but truncated. The 'when' clause is entirely placeholder text ('Use when appropriate context detected') with no actual guidance, making it useless for skill selection. | 1 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'fine-tuning', 'pre-trained model', 'transfer learning', and 'new dataset', but the generic placeholder 'Trigger with relevant phrases based on skill purpose' provides no actual trigger terms and misses common variations users might say. | 2 / 3 |
Distinctiveness Conflict Risk | Transfer learning and fine-tuning are somewhat specific to this domain, but the vague placeholder triggers and truncated description could cause overlap with general ML training or model development skills. | 2 / 3 |
Total | 7 / 12 Passed |
Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.