onnx-converter
Onnx Converter - Auto-activating skill for ML Deployment. Triggers on: onnx converter, onnx converter Part of the ML Deployment skill category.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill onnx-converterOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, essentially serving as a placeholder rather than a functional skill description. It lacks any concrete actions, meaningful trigger terms, or guidance on when Claude should select this skill. The only redeeming quality is that 'ONNX' is a specific enough technology term to provide minimal distinctiveness.
Suggestions
Add specific actions the skill performs, e.g., 'Converts PyTorch, TensorFlow, and Keras models to ONNX format for cross-platform deployment and inference optimization.'
Include a 'Use when...' clause with natural trigger terms like 'convert model to onnx', 'export for inference', 'model portability', 'onnx export', '.onnx files'.
Remove the duplicate trigger term and expand with variations users would naturally say, such as 'model conversion', 'deploy model', 'optimize for production'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool ('Onnx Converter') and category ('ML Deployment') without describing any concrete actions. No specific capabilities like 'convert PyTorch models', 'export TensorFlow graphs', or 'optimize for inference' are mentioned. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and provides no explicit 'when to use' guidance. There is no 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'onnx converter' repeated twice. Missing natural variations users would say like 'convert to onnx', 'export model', 'model conversion', 'pytorch to onnx', 'tensorflow export', or '.onnx'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'ONNX' is fairly specific to a particular ML model format, which provides some distinctiveness. However, 'ML Deployment' is broad and could overlap with other deployment-related skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill is a placeholder template with no actual content about ONNX conversion. It contains only generic meta-descriptions of what a skill should do without any concrete guidance, code examples, or actionable instructions. The content would be completely unhelpful for actually performing ONNX model conversion tasks.
Suggestions
Add executable Python code examples showing how to convert models from common frameworks (PyTorch, TensorFlow) to ONNX format using torch.onnx.export() or tf2onnx
Include a clear workflow with validation steps: export model -> validate with onnx.checker.check_model() -> test inference with onnxruntime
Provide specific guidance on common issues like dynamic axes, opset versions, and unsupported operations with concrete solutions
Remove all generic boilerplate text and replace with actual technical content that Claude doesn't already know
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about ONNX conversion. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that waste tokens without adding value. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does abstractly ('provides step-by-step guidance') but never actually provides any steps, code examples, or executable instructions for ONNX conversion. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps for converting models to ONNX format, no validation checkpoints, and no error handling guidance. The content only describes capabilities without showing how to use them. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative structure with no references to detailed documentation, no links to examples, and no organization that would help Claude navigate to actual implementation details. | 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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