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pathml

Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.

64

Quality

81%

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SecuritybySnyk

Passed

No findings from the security scan

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 excellent progressive disclosure — every reference link is real, one level deep, and clearly signaled — and a solid executable quick-start. Weaknesses are redundancy (the capability list and reference index each appear twice) and the absence of validation checkpoints in the multi-step workflows.

Suggestions

Merge the "References to Detailed Documentation" and "Resources" sections into one reference index to remove the duplicated file listing and tighten the body.

Add a validation checkpoint to each Common Workflow (e.g., after pipeline.run(wsi), verify tile counts and mask keys before downstream analysis; verify trained-model metrics on a held-out set before ONNX deployment).

Add one concrete command or code snippet per Common Workflow (e.g., the Mesmer segmentation call in the CODEX workflow) so the step lists are executable, not just descriptive.

DimensionReasoningScore

Conciseness

Capabilities are enumerated three times ("When to Use This Skill", "Core Capabilities", and the reference-file list repeated in both "References to Detailed Documentation" and "Resources"), so while there is no filler explanation of known concepts, the body could clearly be tightened.

3 / 5

Actionability

Provides executable install commands and a concrete runnable Python example (SlideData.from_slide, Pipeline([...]), pipeline.run(wsi)), but the three "Common Workflows" are step names only ("Collapse multi-run channel data", "Segment cells using Mesmer") without accompanying commands.

4 / 5

Workflow Clarity

Numbered step sequences exist for H&E, CODEX, and ML-training workflows, but there are no validation or verification checkpoints (e.g., checking tiles/masks after pipeline.run or validating a trained model before deployment), matching the anchor for present sequence with missing checkpoints.

3 / 5

Progressive Disclosure

The body is a genuine overview: all six referenced files exist in references/, are substantive (roughly 450-740 lines each), one level deep with no nested references, and are clearly signaled inline ("**See:** `references/preprocessing.md`") plus a summary listing for easy navigation.

5 / 5

Total

15

/

20

Passed

Description

92%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 description: it states concrete capabilities, gives an explicit 'Use for...' trigger clause with natural domain keywords, and even scopes itself against a simpler alternative. The only gap is missing file extensions and a few synonyms that would make triggering even more reliable.

DimensionReasoningScore

Specificity

Names multiple concrete actions — "nucleus segmentation, tissue graph construction, and ML model training", "multiplexed immunofluorescence (CODEX, Vectra)", "Supports 160+ slide formats" — giving comprehensive coverage of the toolkit's domain, matching the top anchor rather than leaving minor gaps.

5 / 5

Completeness

Explicitly answers both "what" (toolkit with named capabilities) and "when" ("Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation...") with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Good natural keyword coverage ("WSI analysis", "nucleus segmentation", "H&E slides", "tile extraction", "pathology data"), but file extensions (.svs, .tif) and common synonyms like "whole-slide images" are missing, so it does not reach the comprehensive-synonyms anchor.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear computational-pathology niche with distinct platform triggers (CODEX, Vectra, WSI) and even disambiguates against a sibling skill ("For simple tile extraction from H&E slides, histolab may be simpler"), so conflict risk is minimal.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/biology/pathml/SKILL.md

Reviewed

Table of Contents

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