smithery.ai

lesion-detection

Classifies skin conditions including Melanoma and Basal Cell Carcinoma using TF.js MobileNetV3

First seen Apr 15, 2026

Installation

$ npx skills add https://smithery.ai

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Skill metadata

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LicenseMIT
Compatibilityopencode
Declared agents opencode
More metadata
audience
developers
workflow
clinical-pipeline

Package contents

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  • skill md SKILL.md 1,465 B
  • docs SUMMARY.md 118 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

What I do

I classify skin conditions using TensorFlow.js with MobileNetV3. I identify patterns consistent with various skin conditions including Melanoma, Basal Cell Carcinoma (BCC), and other dermatoses. I return confidence scores for each classification.

When to use me

Use this when:

  • Feature extraction is complete and you need lesion classification
  • You need confidence scores for risk assessment
  • You're identifying potential areas of concern in skin images

Key Concepts

  • MobileNetV3: TF.js model for skin condition classification
  • Confidence Score: 0-1 probability for each condition
  • Lesion Types: Melanoma, BCC, Actinic Keratosis, etc.
  • lesions_detected: State flag after detection complete

Source Files

  • services/vision.ts: Lesion detection implementation
  • types.ts: AnalysisResult interface with lesions array

Code Patterns

  • Load MobileNetV3 model via TF.js
  • Run inference on feature vectors
  • Return array of detected lesions with confidence and risk levels

Operational Constraints

  • MUST use tf.tidy() or explicit dispose() for all tensors
  • Confidence scores required for all detections
  • High-risk detections trigger elevated scrutiny in downstream