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.
94
92%
Does it follow best practices?
Impact
100%
1.75xAverage score across 3 eval scenarios
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 effectively communicates both capabilities and trigger conditions. It uses third person voice, lists specific technologies and actions, and includes both proactive use cases (deployment, monitoring) and reactive troubleshooting scenarios (latency issues, health check failures). The description is concise yet comprehensive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Deploy ML models with FastAPI, Docker, Kubernetes' and covers specific concerns like 'serving predictions, containerization, monitoring, drift detection, latency issues, health check failures, 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') with explicit trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'FastAPI', 'Docker', 'Kubernetes', 'predictions', 'containerization', 'monitoring', 'drift detection', 'latency issues', 'health check failures', 'version conflicts' - these are all terms users naturally use when facing ML deployment challenges. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on ML model deployment with distinct technology stack (FastAPI, Docker, Kubernetes) and ML-specific concerns (drift detection, serving predictions) that distinguish it from general DevOps or general ML skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, actionable skill with excellent executable code examples and clear workflow guidance. The Known Issues section is particularly valuable for preventing common deployment failures. Minor redundancy between sections (duplicate FastAPI code, overlapping checklists) slightly impacts token efficiency, but overall the content is well-organized with appropriate progressive disclosure to reference files.
Suggestions
Remove the duplicate FastAPI server code - keep either the standalone section or the Quick Start reference, not both
Consolidate the Deployment Checklist and Best Practices sections to eliminate overlap
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some redundancy - the FastAPI server code appears twice (once standalone, once in Quick Start), and the deployment checklist overlaps with best practices. Some sections like the deployment options table add value, but the Known Issues section is quite lengthy. | 2 / 3 |
Actionability | Excellent executable code throughout - complete FastAPI server, Dockerfile, Kubernetes YAML snippets, bash commands, and Python examples are all copy-paste ready. The Known Issues section provides specific problem/solution pairs with concrete code fixes. | 3 / 3 |
Workflow Clarity | The 'Quick Start: Deploy Model in 6 Steps' provides clear sequencing. Known Issues section includes validation patterns (health/readiness probes, Pydantic validation). The deployment checklist and rollback procedures provide explicit checkpoints for error recovery. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections. The 'When to Load References' section explicitly signals one-level-deep references to detailed implementations (fastapi-production-server.md, model-monitoring-drift.md, etc.) with clear descriptions of what each contains. | 3 / 3 |
Total | 11 / 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 |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- secondsky/claude-skills
- Commit
90d6bd7
- Reviewed
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