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.

1.02x

Quality

11%

Does it follow best practices?

Impact

88%

1.02x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/fastapi-ml-endpoint/SKILL.md

Evaluation results

93%

11%

Deploy a Text Classification Model as a REST API

Production-ready ML serving endpoint

Criteria

Without context

With context

FastAPI framework used

100%

Pydantic request model

100%

Pydantic response model

100%

Lifespan model loading

100%

Async endpoint

100%

Health check endpoint

100%

HTTP error handling

100%

Production config present

60%

100%

No hardcoded model path

80%

Inference endpoint defined

100%

Requirements or dependency list

60%

50%

Without context: $0.7026 · 2m 26s · 33 turns · 36 in / 9,053 out tokens

With context: $0.4754 · 1m 49s · 28 turns · 101 in / 6,106 out tokens

95%

Add Operational Observability to an ML Inference Service

MLOps monitoring and observability

Criteria

Without context

With context

Health check endpoint

100%

Readiness vs liveness distinction

100%

Request logging

100%

Structured log format

70%

100%

Inference latency tracking

100%

Request counter or metrics endpoint

100%

Error rate tracking

100%

Model metadata exposed

100%

Production startup logging

70%

80%

FastAPI + middleware approach

50%

70%

Dependency manifest present

100%

Without context: $0.5063 · 2m 4s · 28 turns · 28 in / 7,005 out tokens

With context: $0.5599 · 2m 2s · 32 turns · 113 in / 6,984 out tokens

76%

-11%

Build an End-to-End ML Inference Pipeline Service

MLOps pipeline integration

Criteria

Without context

With context

FastAPI used

100%

Pipeline stages separated

100%

Preprocessing step present

100%

Postprocessing step present

100%

Model loaded at startup

100%

50%

Config from environment

100%

Async or background processing

37%

25%

Pipeline documented

100%

Input validation

60%

Error propagation

55%

66%

Dependency manifest

100%

Without context: $0.9536 · 3m 21s · 42 turns · 45 in / 12,619 out tokens

With context: $0.9219 · 3m 19s · 45 turns · 77 in / 12,354 out tokens

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: f17dd51

Evaluated: 18 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Deploy a Text Classification Model as a REST API Add Operational Observability to an ML Inference Service Build an End-to-End ML Inference Pipeline Service

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