torchserve-config-generator
Torchserve Config Generator - Auto-activating skill for ML Deployment. Triggers on: torchserve config generator, torchserve config generator Part of the ML Deployment skill category.
36
Quality
3%
Does it follow best practices?
Impact
100%
1.00xAverage 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/torchserve-config-generator/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 severely underdeveloped, essentially just restating the skill name without explaining capabilities, use cases, or providing meaningful trigger terms. It fails to help Claude understand when to select this skill over others in an ML deployment context. The redundant trigger terms and lack of concrete actions make this description nearly useless for skill selection.
Suggestions
Add specific actions the skill performs, e.g., 'Generates TorchServe model configuration files including model-config.yaml, config.properties, and handler definitions for deploying PyTorch models'
Include a proper 'Use when...' clause with natural trigger scenarios, e.g., 'Use when deploying PyTorch models to TorchServe, setting up model inference endpoints, or configuring batch inference settings'
Add natural keyword variations users might say: 'torch serve', 'model serving', 'PyTorch deployment', 'inference server', 'model handler', '.mar files'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool ('Torchserve Config Generator') without describing any concrete actions. It doesn't explain what configurations it generates, what parameters it handles, or what outputs it produces. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and the 'when' clause is just a repetition of the skill name rather than describing actual use cases or scenarios when this skill should be selected. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant (same phrase listed twice) and overly specific to the tool name. Missing natural variations users might say like 'model serving config', 'deployment configuration', 'torch serve setup', or 'inference server config'. | 1 / 3 |
Distinctiveness Conflict Risk | The specific mention of 'Torchserve' provides some distinctiveness from generic ML or deployment skills, but the lack of detail about what it actually does could cause confusion with other ML deployment or configuration generation 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 template with no actual content about TorchServe configuration generation. It contains only meta-descriptions of what a skill should do rather than actionable instructions, code examples, or configuration patterns. The content provides zero value for helping Claude generate TorchServe configurations.
Suggestions
Add concrete TorchServe config.properties examples showing common configurations (model store path, inference address, management address, number of workers)
Include executable code for generating model archive (.mar) files with torch-model-archiver command examples
Provide a step-by-step workflow: 1) Create handler, 2) Package model, 3) Generate config, 4) Validate config, 5) Deploy
Add specific configuration patterns for different deployment scenarios (single model, multi-model, GPU vs CPU, batch inference)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains obvious concepts Claude already knows (what triggers are, what capabilities mean). No actual technical content about TorchServe configuration is present. | 1 / 3 |
Actionability | Completely lacks concrete guidance - no code examples, no configuration snippets, no specific commands. Phrases like 'provides step-by-step guidance' describe what the skill should do rather than actually doing it. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps, no sequence, no validation checkpoints. The skill claims to provide 'step-by-step guidance' but contains zero actual steps. | 1 / 3 |
Progressive Disclosure | No structure beyond generic headings. No references to detailed documentation, no links to examples or configuration templates. The content is a shallow placeholder with no depth to disclose. | 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
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- Reviewed
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