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
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 essentially a title and category label with no substantive content. It fails to describe what the skill actually does, provides no natural trigger terms beyond the skill name repeated, and lacks any explicit guidance on when Claude should select it. It would be nearly useless for skill selection among a large set of skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Generates TorchServe configuration files (config.properties), creates model archive (.mar) files, configures batch size, worker threads, and inference endpoints.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about deploying PyTorch models with TorchServe, generating serving configs, setting up model archives, or configuring inference endpoints.'
Remove the duplicate trigger term and expand with natural variations users might say, such as 'TorchServe setup', 'model serving configuration', 'PyTorch deployment', 'config.properties', '.mar file'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('ML Deployment') and mentions 'Torchserve Config Generator' but does not describe any concrete actions. There are no specific capabilities listed such as generating config files, setting model parameters, configuring endpoints, etc. | 1 / 3 |
Completeness | The description barely addresses 'what does this do' beyond the name itself, and there is no explicit 'when should Claude use it' clause. The 'Triggers on' line is just a duplicate of the skill name, not meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'torchserve config generator' repeated twice. It lacks natural variations users might say such as 'TorchServe configuration', 'model serving config', 'deploy PyTorch model', '.mar file', 'model archiver', or 'inference endpoint setup'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'torchserve' is fairly specific to a particular ML serving framework, which provides some distinctiveness. However, the vague 'ML Deployment' category and lack of concrete actions could cause overlap with other ML deployment or model serving 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 a hollow template with no substantive content. It contains no executable code, no TorchServe configuration examples, no actual guidance on generating configs, and no references to supporting materials. Every section restates the skill name without providing any actionable information about TorchServe configuration generation.
Suggestions
Add a concrete, executable example showing how to generate a TorchServe config.properties file with key parameters (e.g., inference_address, management_address, model_store, number_of_gpu, job_queue_size).
Include a step-by-step workflow: 1) Define model archive, 2) Generate config.properties, 3) Validate config, 4) Start TorchServe with the config, with validation checkpoints at each stage.
Provide a complete config.properties template with annotated fields covering common deployment scenarios (single model, multi-model, GPU/CPU, batch inference).
Remove all boilerplate sections ('When to Use', 'Example Triggers', 'Capabilities') that contain no actionable information and replace with actual TorchServe-specific content.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats the skill name excessively, and provides zero substantive information about TorchServe configuration generation. | 1 / 3 |
Actionability | There is no concrete guidance whatsoever—no code, no commands, no configuration examples, no TorchServe config file format, no parameters. Every bullet is vague and abstract ('Provides step-by-step guidance' without actually providing any). | 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 none. | 1 / 3 |
Progressive Disclosure | There is no meaningful content to organize, no references to supporting files, and no bundle files exist. The structure is a series of empty-calorie sections with no navigational value. | 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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