torchscript-exporter
Torchscript Exporter - Auto-activating skill for ML Deployment. Triggers on: torchscript exporter, torchscript exporter Part of the ML Deployment skill category.
35
3%
Does it follow best practices?
Impact
95%
0.95xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/torchscript-exporter/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 title and category label with no substantive content. It fails to describe what the skill does, provides no meaningful trigger terms beyond the skill's own name (duplicated), and lacks any 'Use when...' guidance. It would be nearly useless for Claude to differentiate this skill from others in a large skill library.
Suggestions
Add concrete action descriptions such as 'Converts PyTorch models to TorchScript format using tracing or scripting for production deployment'.
Add an explicit 'Use when...' clause, e.g., 'Use when the user wants to export a PyTorch model to TorchScript, convert models for production serving, or optimize models with torch.jit'.
Include natural trigger term variations users would actually say: 'TorchScript', 'torch.jit', 'export PyTorch model', 'model tracing', 'model scripting', 'production deployment', '.pt file'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('ML Deployment') and mentions 'torchscript exporter' but does not describe any concrete actions. There are no verbs indicating what the skill actually does (e.g., 'converts PyTorch models to TorchScript', 'traces or scripts models for deployment'). | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and the 'when' clause is essentially just the skill name repeated. There is no explicit 'Use when...' guidance with meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | The only trigger term listed is 'torchscript exporter' repeated twice. It misses natural variations users would say such as 'torch.jit', 'export model', 'TorchScript', 'model tracing', 'model scripting', 'deploy PyTorch model', '.pt file', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'torchscript exporter' is fairly niche and unlikely to conflict with many other skills, but the lack of specificity about what it does versus other ML deployment skills (e.g., ONNX export, model serving) means there could be overlap within the ML Deployment category. | 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 actual instructional content about TorchScript exporting. It contains only meta-descriptions of what the skill would do without providing any concrete guidance, code examples, or workflows. It would be entirely unhelpful to Claude in performing TorchScript export tasks.
Suggestions
Add executable Python code examples showing both `torch.jit.trace()` and `torch.jit.script()` approaches with a simple model, including saving and loading the exported model.
Define a clear multi-step workflow: prepare model (eval mode, dummy input) → export (trace vs script) → validate (load and run inference) → optimize (optional), with explicit validation checkpoints.
Remove all the meta-content (Purpose, When to Use, Capabilities, Example Triggers) and replace with actionable content covering common pitfalls like dynamic control flow, unsupported operations, and input shape constraints.
Add a brief section on choosing between tracing and scripting, with concrete criteria and examples of when each is appropriate.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler with no substantive information. It explains what the skill does in abstract terms without providing any actual guidance on TorchScript exporting. Every section restates the same vague idea. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific steps for exporting a model to TorchScript. Phrases like 'Provides step-by-step guidance' and 'Generates production-ready code' describe capabilities without demonstrating them. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, and no validation checkpoints for what is inherently a multi-step process (model preparation, tracing/scripting, validation, saving). | 1 / 3 |
Progressive Disclosure | The content is a flat, shallow document with no references to detailed materials, no examples section, and no links to related resources. There is nothing to progressively disclose because there is no substantive 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
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.