tensorflow-model-trainer
Tensorflow Model Trainer - Auto-activating skill for ML Training. Triggers on: tensorflow model trainer, tensorflow model trainer Part of the ML Training skill category.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill tensorflow-model-trainerOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, consisting mostly of boilerplate metadata rather than actionable content. It fails to describe any concrete capabilities, lacks natural trigger terms users would actually say, and provides no guidance on when Claude should select this skill. The redundant trigger term and category label provide minimal value for skill selection.
Suggestions
Add specific actions the skill performs, e.g., 'Builds, trains, and evaluates TensorFlow/Keras neural networks. Handles data preprocessing, model architecture definition, hyperparameter tuning, and training loop management.'
Add a 'Use when...' clause with natural trigger terms: 'Use when the user mentions training neural networks, deep learning models, TensorFlow, Keras, model fitting, epochs, or .h5 files.'
Include common user phrases and file types: 'train a model', 'neural network', 'deep learning', 'CNN', 'RNN', 'loss function', 'optimizer', '.pb files', 'SavedModel'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('ML Training', 'Tensorflow Model Trainer') but provides no concrete actions. It doesn't describe what the skill actually does - no mention of training workflows, model architectures, data handling, or any specific capabilities. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the vague 'ML Training' category label. There is no 'Use when...' clause or explicit trigger guidance - only a redundant trigger list that doesn't explain when to use the skill. | 1 / 3 |
Trigger Term Quality | The trigger terms listed are just 'tensorflow model trainer' repeated twice. Missing natural user terms like 'train neural network', 'deep learning', 'fit model', 'epochs', 'TensorFlow', 'keras', '.h5', 'model training', etc. | 1 / 3 |
Distinctiveness Conflict Risk | While 'TensorFlow' provides some specificity that distinguishes it from generic ML skills, the lack of detail about what TensorFlow operations it handles (training vs inference, specific model types) could cause overlap with other ML-related skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill content is essentially a placeholder template with no actual instructional value. It contains only meta-descriptions of what a TensorFlow training skill should do without providing any concrete guidance, code examples, or workflows. The content fails to teach Claude anything about TensorFlow model training.
Suggestions
Add executable TensorFlow code examples showing model definition, compilation, and training (e.g., tf.keras.Sequential model with model.fit())
Define a clear workflow with steps: 1) Data loading/preprocessing, 2) Model architecture, 3) Training configuration, 4) Training loop with checkpoints, 5) Evaluation
Include specific guidance on hyperparameter tuning (learning rate, batch size, epochs) with concrete values or ranges
Add validation checkpoints such as verifying data shapes, monitoring loss curves, and saving/loading model checkpoints
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information. | 1 / 3 |
Actionability | There is zero concrete guidance - no code, no commands, no specific examples of TensorFlow model training. The content describes what the skill supposedly does but never actually instructs how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is defined. For a model training skill, there should be clear steps for data preparation, model definition, training loops, checkpointing, and evaluation - none of which are present. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative document with no structure pointing to detailed materials. There are no references to examples, API documentation, or advanced topics despite claiming to cover data preparation, training, hyperparameter tuning, and experiment tracking. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%Validation — 11 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
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 |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Reviewed
Table of Contents
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