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.
27
11%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/ai-ml/transfer-learning-adapter/skills/adapting-transfer-learning-models/SKILL.mdQuality
Discovery
22%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description suffers from being truncated mid-sentence and padded with meaningless boilerplate trigger guidance ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.'). While it identifies a reasonably specific domain (transfer learning / fine-tuning), the lack of concrete actions, the incomplete sentence, and the entirely generic 'when' clause make it ineffective for skill selection.
Suggestions
Complete the truncated sentence and list specific concrete actions (e.g., 'freeze/unfreeze layers, adjust learning rates, evaluate on new domain data, apply domain adaptation').
Replace the generic boilerplate 'Use when appropriate context detected' with explicit trigger conditions (e.g., 'Use when the user mentions fine-tuning, transfer learning, adapting a pre-trained model, domain adaptation, or retraining on a new dataset').
Add specific file types, frameworks, or model types to improve distinctiveness (e.g., 'PyTorch, TensorFlow, HuggingFace models, .pt/.h5 checkpoints').
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | While it mentions 'transfer learning techniques' and 'fine-tuning a model', the description is largely vague with phrases like 'Use when appropriate context detected' and 'Trigger with relevant phrases based on skill purpose' which are meaningless filler rather than concrete actions. | 1 / 3 |
Completeness | The 'what' is partially stated but truncated mid-sentence ('performing...'), and the 'when' clause is pure boilerplate ('Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.') providing zero actionable guidance. This fails to answer either question adequately. | 1 / 3 |
Trigger Term Quality | Contains some relevant keywords like 'fine-tuning', 'pre-trained model', 'transfer learning', and 'new dataset' that users might naturally say, but the description is truncated ('performing...') and the trigger guidance is entirely generic boilerplate rather than listing actual trigger terms. | 2 / 3 |
Distinctiveness Conflict Risk | The domain of transfer learning and fine-tuning pre-trained models is somewhat specific, but the truncated and vague description could overlap with general ML training skills, model evaluation skills, or data processing skills. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a template filled with generic placeholder content. It contains no executable code, no concrete implementation details, and no specific guidance for performing transfer learning. Nearly every section reads as boilerplate that could apply to any skill, with the actual domain-specific knowledge (model architectures, framework-specific code, hyperparameter configurations, layer freezing strategies) entirely absent.
Suggestions
Replace the abstract 'How It Works' section with concrete, executable Python code examples showing actual transfer learning workflows (e.g., PyTorch ResNet fine-tuning with layer freezing, HuggingFace BERT adaptation) that Claude can directly use or adapt.
Remove all generic boilerplate sections ('Prerequisites', 'Instructions', 'Output', 'Error Handling', 'Resources', 'Integration') that contain no skill-specific information and waste token budget.
Add a clear workflow with validation checkpoints, e.g., data shape verification before training, loss monitoring during training, and evaluation metric thresholds to determine if fine-tuning succeeded.
Include specific, actionable best practices with code snippets—e.g., concrete learning rate schedules, specific layer freezing patterns for different model architectures, and discriminative fine-tuning examples—rather than generic advice like 'experiment with different hyperparameters'.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanation of concepts Claude already knows. Sections like 'Overview', 'How It Works', 'When to Use This Skill', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' are all padded filler that provide no actionable information. The skill explains what transfer learning is and how it works conceptually rather than providing concrete implementation details. | 1 / 3 |
Actionability | No executable code anywhere in the skill. The examples describe what the skill 'will do' in abstract terms rather than providing concrete code, commands, or copy-paste-ready snippets. Instructions like 'Invoke this skill when the trigger conditions are met' and 'Provide necessary context and parameters' are completely vague and non-actionable. | 1 / 3 |
Workflow Clarity | The 'How It Works' section lists abstract steps like 'Analyze Requirements' and 'Generate Adaptation Code' without any concrete implementation details, validation checkpoints, or error recovery loops. The 'Instructions' section is a generic 4-step placeholder with no specificity. For a skill involving model training (a multi-step, resource-intensive process), there are no validation steps or feedback loops. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no bundle files and no meaningful references to external resources. The 'Resources' section lists 'Project documentation' and 'Related skills and commands' without any actual links or file paths. Content is poorly organized with many boilerplate sections that add no value. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 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 | 9 / 11 Passed | |
- Commit
3a2d27d
- Reviewed
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