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.
Install with Tessl CLI
npx tessl i github:secondsky/claude-skills --skill model-deployment90
Does it follow best practices?
Validation for skill structure
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 combines specific technologies (FastAPI, Docker, Kubernetes) with concrete use cases and problem scenarios that users would naturally describe. The description is concise yet comprehensive, covering the deployment lifecycle from containerization to monitoring and troubleshooting.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Deploy ML models with FastAPI, Docker, Kubernetes' and specific use cases like 'serving predictions, containerization, monitoring, drift detection' plus concrete problems like '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 guidance. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'ML models', 'FastAPI', 'Docker', 'Kubernetes', 'predictions', 'containerization', 'monitoring', 'drift detection', 'latency issues', 'health check failures', 'version conflicts' - all terms a user would naturally mention when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on ML deployment infrastructure with distinct triggers combining ML models + deployment tools (FastAPI, Docker, Kubernetes) + MLOps-specific concerns (drift detection, model serving). Unlikely to conflict with general coding or generic Docker 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, production-ready skill with excellent actionability and workflow clarity. The Known Issues Prevention section is particularly valuable, providing concrete solutions to common deployment problems. Minor improvements could be made by reducing redundancy (FastAPI server appears twice) and trimming some explanatory text, but overall the skill effectively balances comprehensiveness with usability.
Suggestions
Remove the duplicate FastAPI server code - keep only the more complete version in the Known Issues section or consolidate into one canonical example
Trim the deployment options table and ModelMonitor stub since they add little actionable value compared to the detailed implementations in references
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some redundancy - the deployment options table and basic concepts could be trimmed. The Known Issues section is valuable but verbose in places, and some content (like the basic FastAPI server) appears twice in different forms. | 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 6-step quick start and Known Issues sections provide concrete, specific guidance. | 3 / 3 |
Workflow Clarity | The 6-step deployment workflow is clearly sequenced with explicit commands. The Known Issues section provides excellent validation patterns (health checks, readiness probes, input validation). The deployment checklist and rollback procedures address error recovery. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections and a dedicated 'When to Load References' section that signals one-level-deep references to detailed implementations. The main content provides actionable overview while pointing to specific reference files for deep dives. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
75%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 12 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | 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 |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 12 / 16 Passed | |
- Reviewed
Table of Contents
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