model-versioning-manager
Model Versioning Manager - Auto-activating skill for ML Deployment. Triggers on: model versioning manager, model versioning manager Part of the ML Deployment skill category.
34
3%
Does it follow best practices?
Impact
86%
0.96xAverage 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/model-versioning-manager/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 extremely weak across all dimensions. It reads as an auto-generated stub with no concrete actions, no meaningful trigger terms (the same phrase is duplicated), and no 'Use when...' guidance. It provides almost no information for Claude to determine when to select this skill over others.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Tracks ML model versions, manages model registries, compares model performance across versions, handles rollbacks and promotions between staging and production environments.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about model versioning, model registry, deploying a specific model version, rolling back a model, or managing ML model lifecycle.'
Remove the duplicated trigger term and replace with diverse natural language variations users would actually say, such as 'model version', 'model registry', 'deploy model', 'rollback model', 'model lifecycle', 'ML versioning'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('Model Versioning Manager') but describes no concrete actions. There are no specific capabilities listed such as 'track model versions', 'compare model performance', or 'rollback deployments'. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause with meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'model versioning manager' repeated twice. These are not natural phrases users would say; users would more likely say things like 'version my model', 'track model versions', 'model registry', 'deploy model version', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'ML Deployment' and 'Model Versioning' provides some domain specificity that narrows the scope, but without concrete actions described, it could still 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 shell with no substantive content. It consists entirely of auto-generated boilerplate that repeats the phrase 'model versioning manager' without providing any actual guidance on model versioning, ML deployment, or any related topic. It fails on every dimension because there is simply no real content to evaluate.
Suggestions
Add concrete, executable code examples for model versioning workflows (e.g., using MLflow, DVC, or W&B for registering, tagging, and promoting model versions)
Define a clear multi-step workflow for model versioning lifecycle: register → validate → stage → promote to production, with validation checkpoints at each stage
Include specific commands and configuration examples (e.g., `mlflow models serve`, model registry API calls, version comparison scripts)
Replace all generic boilerplate sections with actual domain-specific content covering model artifact storage, version metadata, rollback procedures, and A/B testing between model versions
| 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 knowledge or instructions that Claude doesn't already know. Every section is generic boilerplate that could apply to any skill. | 1 / 3 |
Actionability | There is zero concrete, executable guidance — no code, no commands, no specific steps, no examples of model versioning workflows. The content only describes what the skill claims to do without actually doing it. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequences, no validation checkpoints — just vague claims like 'provides step-by-step guidance' without any actual steps. | 1 / 3 |
Progressive Disclosure | No references to external files, no structured content hierarchy, and no meaningful organization. The sections are just repetitive restatements of the skill name with no real content to organize. | 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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