deploying-machine-learning-models
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill deploying-machine-learning-modelsDeploy this skill enables AI assistant to deploy machine learning models to production environments. it automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. use this skill when th... Use when deploying or managing infrastructure. Trigger with phrases like 'deploy', 'infrastructure', or 'CI/CD'.
Validation
81%| Criteria | Description | Result |
|---|---|---|
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 |
Total | 13 / 16 Passed | |
Implementation
0%This skill content is essentially a template with placeholder text that provides no actionable guidance for deploying ML models. It lacks any concrete code, specific commands, tool names, or executable examples. The content explains concepts Claude already knows while failing to provide the specific, project-relevant information that would make this skill useful.
Suggestions
Replace abstract descriptions with executable code examples showing actual deployment commands (e.g., specific Docker commands, FastAPI endpoint code, kubectl deployment manifests)
Remove redundant and generic sections (duplicate Overview, generic Prerequisites, placeholder Instructions/Output/Error Handling) that add no specific value
Add concrete tool specifications - which serving platform, which containerization approach, which orchestration system - with actual configuration examples
Include validation checkpoints in the workflow (e.g., 'Test endpoint locally before deploying', 'Verify model loads correctly', 'Run health check after deployment')
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with redundant sections (Overview repeated twice), explains obvious concepts Claude knows (what deployment is, what APIs are), and includes generic boilerplate sections (Prerequisites, Instructions, Output, Error Handling) that add no specific value. | 1 / 3 |
Actionability | No executable code, no specific commands, no concrete examples. Everything is abstract description ('The skill will generate code', 'Deploy to a cloud-based serving platform') without any actual implementation details or copy-paste ready content. | 1 / 3 |
Workflow Clarity | Steps are vague and non-actionable ('Analyze Requirements', 'Generate Code', 'Deploy Model'). No validation checkpoints, no specific tools or commands mentioned, no error recovery workflows. The examples describe what will happen but not how. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files. Content is poorly organized with redundant sections and generic placeholders. The 'Resources' section mentions 'Project documentation' and 'Related skills' without any actual links or specifics. | 1 / 3 |
Total | 4 / 12 Passed |
Activation
67%The description provides reasonable coverage of what the skill does and includes explicit trigger guidance, which is good. However, it suffers from truncation (appears cut off at 'th...'), uses somewhat generic action language, and the trigger terms are too broad which could cause conflicts with other deployment-related skills. The ML-specific focus is not strongly reinforced in the trigger terms.
Suggestions
Add ML-specific trigger terms like 'model serving', 'inference endpoint', 'containerize model', or 'production model' to reduce conflict with generic deployment skills.
Replace vague phrases like 'implements best practices' and 'optimizes performance' with concrete actions such as 'configures auto-scaling', 'sets up model endpoints', or 'implements A/B testing'.
Fix the truncated text and ensure the full description is properly formatted without cut-off content.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (ML model deployment) and some actions (automates deployment workflow, implements best practices, optimizes performance, handles errors), but these are somewhat generic and not highly concrete actions like specific deployment targets or methods. | 2 / 3 |
Completeness | Explicitly answers both what (deploy ML models, automate workflow, optimize performance) AND when with a clear 'Use when...' clause specifying triggers like 'deploy', 'infrastructure', or 'CI/CD'. | 3 / 3 |
Trigger Term Quality | Includes some relevant keywords ('deploy', 'infrastructure', 'CI/CD') but missing common ML deployment variations like 'model serving', 'production', 'containerize', 'kubernetes', 'endpoint', or 'inference'. | 2 / 3 |
Distinctiveness Conflict Risk | The triggers 'deploy' and 'infrastructure' are quite broad and could conflict with general DevOps or cloud infrastructure skills. The ML model focus provides some distinction but 'CI/CD' is very generic. | 2 / 3 |
Total | 9 / 12 Passed |
Reviewed
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