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.
36
3%
Does it follow best practices?
Impact
100%
1.56xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/tensorflow-model-trainer/SKILL.mdQuality
Discovery
7%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 is extremely weak—it reads as an auto-generated template with no substantive content. It lacks concrete actions, meaningful trigger terms, and any explicit guidance on when Claude should select this skill. The repeated trigger term and boilerplate category mention provide almost no useful information for skill selection.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Builds, trains, and evaluates TensorFlow/Keras neural network models, configures layers, tunes hyperparameters, and exports saved models.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to train a neural network, build a TensorFlow model, use Keras, fine-tune a deep learning model, or work with .h5/.pb model files.'
Remove the redundant duplicate trigger term and replace with diverse natural keywords users would actually say, such as 'deep learning', 'neural network', 'keras', 'model training', 'epochs', 'loss function'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('ML Training', 'Tensorflow') but provides no concrete actions. There is no mention of what the skill actually does—no specific capabilities like 'trains models', 'tunes hyperparameters', 'loads datasets', etc. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause—only a redundant trigger phrase. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'tensorflow model trainer' repeated twice. There are no natural user keywords like 'train a model', 'TensorFlow', 'deep learning', 'neural network', 'fit model', '.h5', 'keras', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Tensorflow' provides some specificity that distinguishes it from generic ML skills, but 'ML Training' is broad and could overlap with PyTorch, scikit-learn, or other training-related skills. Without concrete actions, the distinction is weak. | 2 / 3 |
Total | 5 / 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 an empty template with no substantive content. It contains only generic boilerplate descriptions that repeat the skill name without providing any actual instructions, code examples, workflows, or technical guidance for TensorFlow model training. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable TensorFlow code examples covering common training workflows (e.g., data loading with tf.data, model definition with tf.keras, training loop with model.fit, and checkpointing).
Define a clear multi-step workflow with validation checkpoints, such as: data preparation → model architecture → compilation → training → evaluation → export, with specific commands and verification steps at each stage.
Remove all generic boilerplate ('This skill provides automated assistance...', 'Provides step-by-step guidance...') and replace with actual technical content that Claude doesn't already know, such as project-specific conventions, preferred hyperparameter ranges, or experiment tracking patterns.
Add references to separate files for advanced topics like hyperparameter tuning strategies, distributed training setup, and experiment tracking integration (e.g., with TensorBoard or MLflow).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'tensorflow model trainer' excessively, and provides zero actual technical content or instructions. | 1 / 3 |
Actionability | There is no concrete code, no executable commands, no specific examples, and no actual guidance on how to train a TensorFlow model. Every section is vague and abstract. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequences, no validation checkpoints—just generic claims about providing 'step-by-step guidance' without actually providing any. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic block of generic text with no structure pointing to detailed resources, no references to external files, and no meaningful organization of content. | 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
87f14eb
- Reviewed
Table of Contents
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