fastapi-ml-endpoint
Fastapi Ml Endpoint - Auto-activating skill for ML Deployment. Triggers on: fastapi ml endpoint, fastapi ml endpoint Part of the ML Deployment skill category.
34
3%
Does it follow best practices?
Impact
88%
1.02xAverage 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/fastapi-ml-endpoint/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 essentially a placeholder that repeats the skill name and category without providing any meaningful information about what the skill does or when it should be used. It lacks concrete actions, natural trigger terms, and explicit usage guidance, making it nearly useless for skill selection among multiple options.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates FastAPI endpoints for serving ML model predictions, handles request/response schemas, and configures model loading and inference pipelines.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user wants to deploy a machine learning model as a REST API, create prediction endpoints, serve model inference via FastAPI, or build an ML microservice.'
Remove the duplicate trigger term and expand with natural variations users would say, such as 'deploy model API', 'serve predictions', 'model endpoint', 'inference API', 'ML REST service'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions. It only names the domain ('ML Deployment') and repeats the skill name without describing what the skill actually does (e.g., create endpoints, deploy models, configure routes). | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is meaningfully answered. There is no 'Use when...' clause and no description of capabilities beyond the category label 'ML Deployment'. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('fastapi ml endpoint, fastapi ml endpoint'). There are no natural user-language variations like 'deploy model', 'serve predictions', 'API endpoint', 'model serving', or 'REST API'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'fastapi' and 'ml endpoint' together provides some specificity that narrows the niche, but the lack of concrete actions or clear triggers means it could still overlap with general FastAPI or general ML deployment 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 placeholder with no substantive content. It contains only generic boilerplate descriptions that repeat the skill name without providing any actual guidance on building FastAPI ML endpoints—no code examples, no architecture patterns, no deployment steps, no model serving configurations. It fails on every dimension of the rubric.
Suggestions
Add executable FastAPI code examples showing a complete ML endpoint (e.g., a /predict route with Pydantic request/response models, model loading, and inference).
Include a clear workflow: model loading strategy → endpoint definition → input validation → inference → response formatting → health checks, with validation steps.
Remove all generic boilerplate ('This skill provides automated assistance...') and replace with concrete, domain-specific guidance on topics like async inference, batching, model versioning, and GPU resource management.
Add references to separate files for advanced topics (e.g., MONITORING.md for Prometheus metrics, DEPLOYMENT.md for Docker/K8s configs) to demonstrate progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know and provides zero domain-specific information about FastAPI ML endpoints. Every section restates the same vague idea. | 1 / 3 |
Actionability | There is no concrete code, no commands, no specific examples, no API patterns, no model serving snippets—nothing actionable whatsoever. It only describes what the skill could do in abstract terms. | 1 / 3 |
Workflow Clarity | No workflow, no steps, no sequence, no validation checkpoints. The content mentions 'step-by-step guidance' but provides none. | 1 / 3 |
Progressive Disclosure | No references to external files, no structured navigation, and the content itself is a flat wall of generic placeholder text with no meaningful organization of information. | 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
3076d78
- Reviewed
Table of Contents
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