model-deployment
Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
88
86%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 is a strong skill description that concisely communicates both the capability scope (ML model deployment with specific technologies) and when to use it (serving, containerization, monitoring, and specific failure scenarios). It uses third-person voice correctly and includes excellent trigger terms covering both proactive tasks and reactive troubleshooting scenarios.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: deploying ML models, serving predictions, containerization, monitoring, drift detection. Also mentions specific troubleshooting scenarios like latency issues, health check failures, and version conflicts. | 3 / 3 |
Completeness | Clearly answers both 'what' (deploy ML models with FastAPI, Docker, Kubernetes) and 'when' (Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts). The 'Use for...' clause serves as an explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'ML models', 'FastAPI', 'Docker', 'Kubernetes', 'predictions', 'containerization', 'monitoring', 'drift detection', 'latency issues', 'health check failures', 'version conflicts'. These cover both the technology stack and common problem scenarios. | 3 / 3 |
Distinctiveness Conflict Risk | Occupies a clear niche at the intersection of ML deployment and DevOps/infrastructure tooling (FastAPI, Docker, Kubernetes). The specific technology stack and ML-serving focus make it highly distinguishable from general coding, general ML/training, or general DevOps skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive ML deployment skill with strong actionability and good progressive disclosure through well-signaled reference files. Its main weaknesses are moderate verbosity (redundant code blocks, some content Claude already knows) and missing explicit validation checkpoints in the deployment workflow. The Known Issues section is a standout strength, providing concrete problem-solution pairs for common deployment failures.
Suggestions
Remove redundant code: the FastAPI server and Dockerfile appear in both the main sections and the Quick Start—consolidate into one location and reference it.
Add explicit validation checkpoints to the Quick Start workflow (e.g., 'curl localhost:8000/health' after step 4, verify docker build exit code, check kubectl rollout status before declaring success).
Remove the deployment options table at the top—it explains concepts Claude already knows and doesn't add actionable guidance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is quite long (~200+ lines) with some redundancy—the FastAPI server code appears in multiple sections, the Dockerfile is shown twice, and the deployment options table at the top adds little value Claude doesn't already know. The Known Issues section is valuable but could be more compact. | 2 / 3 |
Actionability | The skill provides fully executable code throughout: complete FastAPI server with Pydantic models, working Dockerfile, kubectl commands, CI/CD YAML, and Kubernetes resource configs. Code is copy-paste ready with concrete examples for each scenario. | 3 / 3 |
Workflow Clarity | The 'Quick Start: Deploy Model in 6 Steps' provides a clear sequence, and the Known Issues section covers error recovery well. However, there are no explicit validation checkpoints between steps (e.g., verify the Docker build succeeded, test the API before pushing to registry, validate the model loads correctly before deploying to K8s). | 2 / 3 |
Progressive Disclosure | The 'When to Load References' section clearly signals four one-level-deep reference files with specific descriptions of what each contains. The main skill provides a solid overview with actionable content while deferring detailed implementations to well-described reference files. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- secondsky/claude-skills
- Commit
88da5ff
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.