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pathml

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.

68

Quality

82%

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SKILL.md
Quality
Evals
Security

Quality

Content

65%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Well-structured overview with strong progressive disclosure and an executable quick start, weakened by redundant reference listings and abstract multi-step workflows lacking validation checkpoints.

Suggestions

Eliminate the triple reference listing: keep a single references section and rely on the per-capability 'See:' pointers rather than restating all six files in both 'References to Detailed Documentation' and 'Resources'.

Tighten 'Common Workflows' into concrete, executable steps (with real class/method names like CODEXSlide and Mesmer) and add a verification checkpoint after segmentation or training (e.g., inspect tile.masks keys or check validation loss).

Trim the Overview paragraph since it duplicates the frontmatter description; lead directly into capabilities.

DimensionReasoningScore

Conciseness

Mostly efficient and free of basic-concept padding, but the Overview re-states the description and the six reference files are listed three separate times (per-capability 'See:' lines, 'References to Detailed Documentation', and the 'Resources/references/' block), which could be tightened.

3 / 5

Actionability

Provides a copy-paste-ready Quick Start code example with real imports (SlideData, Pipeline, TissueDetectionHE, StainNormalizationHE) and names concrete transforms/models; the 'Common Workflows' step lists remain somewhat high-level, a minor gap.

4 / 5

Workflow Clarity

Numbered sequences are present for H&E, CODEX, and ML-training workflows, but steps are abstract ('Apply tissue detection and stain normalization') with no validation or verification checkpoints, which caps clarity for these batch/analytical pipelines.

3 / 5

Progressive Disclosure

SKILL.md is a clear overview that splits detail into six well-signaled, one-level-deep reference files, all of which exist in references/; navigation is easy with explicit per-section 'See:' pointers.

5 / 5

Total

15

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that concretely states both what the toolkit does and when to use it, with rich natural trigger terms and minimal conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'analyzing whole-slide images', 'nucleus detection/segmentation', 'tissue graph construction', 'training ML models' — giving comprehensive coverage of the toolkit's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' ('Computational pathology toolkit for analyzing whole-slide images...') and 'when' ('Use this skill when working with histopathology slides, H&E stained images...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms including synonyms and format names: 'histopathology slides', 'H&E stained images', 'multiplex immunofluorescence (CODEX, Vectra)', 'spatial proteomics', plus concrete formats 'Aperio SVS, NDPI, DICOM, OME-TIFF'.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear computational-pathology niche with highly specific triggers (WSI, CODEX, HoVer-Net-style work) that are unlikely to fire for adjacent skills, and the body's routing boundary further reduces overlap.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
foryourhealth111-pixel/Vibe-Skills
Reviewed

Table of Contents

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