model-pruning-helper
Model Pruning Helper - Auto-activating skill for ML Deployment. Triggers on: model pruning helper, model pruning helper Part of the ML Deployment skill category.
21
3%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/model-pruning-helper/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 essentially a placeholder that provides almost no useful information for skill selection. It repeats its own name as trigger terms, lists no concrete actions or capabilities, and lacks any meaningful 'when to use' guidance. It would be nearly impossible for Claude to reliably select this skill based on user intent.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Applies structured and unstructured pruning to neural network models, analyzes layer-wise sparsity, evaluates accuracy-compression tradeoffs, and exports pruned models for deployment.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about reducing model size, pruning weights, compressing neural networks, improving inference speed, or mentions sparsity, magnitude pruning, or lottery ticket hypothesis.'
Remove the redundant duplicate trigger term and replace with diverse natural language variations users would actually say, such as 'prune model', 'reduce parameters', 'model compression', 'weight removal', 'sparse model'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('model pruning') but describes no concrete actions whatsoever. There are no specific capabilities listed like 'prune layers', 'analyze sparsity', or 'reduce model size'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and the 'when' clause is just a self-referential trigger on its own name rather than describing meaningful use cases or scenarios. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are the redundant 'model pruning helper' repeated twice. It lacks natural user keywords like 'prune model', 'reduce parameters', 'sparsity', 'compress model', 'weight pruning', or 'inference optimization'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'model pruning' is somewhat specific to a niche ML task, which provides some distinctiveness. However, the vague 'ML Deployment' category and lack of concrete actions could cause overlap with other ML-related skills. | 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 placeholder that provides no actual instruction on model pruning. It consists entirely of meta-descriptions and trigger phrases, with no executable code, concrete techniques, or actionable guidance. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable code examples for common pruning techniques (e.g., magnitude pruning with PyTorch's torch.nn.utils.prune, structured pruning, or TensorFlow Model Optimization Toolkit).
Define a clear multi-step workflow: select pruning strategy → apply pruning → evaluate accuracy/latency tradeoff → fine-tune → export optimized model, with validation checkpoints at each stage.
Remove all meta-description sections (Purpose, When to Use, Example Triggers, Capabilities) and replace with actual technical content—specific pruning methods, sparsity targets, and deployment considerations.
Add references to detailed guides for advanced topics (e.g., knowledge distillation after pruning, hardware-aware pruning, quantization-aware pruning) as separate linked files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague idea ('model pruning helper') without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific techniques for model pruning. The content only describes what the skill would do rather than actually instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The 'step-by-step guidance' is merely claimed in a bullet point but never provided. There are no validation checkpoints or sequences. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive document with no meaningful structure. There are no references to detailed materials, no links to related files, and the sections are superficial headers over vacuous 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
3076d78
- Reviewed
Table of Contents
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