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histolab

Extracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.

Quality

94%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

Excellent body content: executable install and quick-start recipes, a well-sequenced workflow with genuine validation checkpoints for batch tile extraction, and a clean one-level reference structure. The only trimmable material is the verification-scope meta commentary and the lengthy citation instructions.

Suggestions

Move the 'The review checked the published 0.7.0 source because the current Read the Docs pages still display 0.6.0...' verification-scope paragraph into a reference file (or the frontmatter metadata), keeping the body focused on instructions.

Condense the citation section to the reference itself plus a one-line fetch-and-cite instruction; the detailed arXiv API fallback and version-suffix rules could live in a references file.

Trim niche environment-specific workarounds (e.g., the DYLD_FALLBACK_LIBRARY_PATH macOS detail) into the installation reference so the main path stays minimal.

DimensionReasoningScore

Conciseness

The body is dense and assumes Claude's competence (e.g., 'extraction_mask belongs to extract() and locate_tiles(), not to the tiler constructor'), with no explanation of known concepts. Minor trimmable parts remain: the 'References and verification scope' meta paragraph and the extended citation/fetch instructions, which keeps it below the 'every token earns its place' anchor.

4 / 5

Actionability

Fully executable guidance: complete install commands with an import-verification step ('uv venv --python 3.10 ...', "python -c 'import openslide; print(...)'"), a copy-paste-ready quick start covering the common case, and troubleshooting bullets naming concrete parameters (tissue_percent, max_iter, pixel_overlap).

5 / 5

Workflow Clarity

A clear 6-step workflow with explicit validation checkpoints for a batch operation — 'Count saved files, inspect representative tiles', preview 'with the **same mask** passed to extraction', 'inspect raw scores and finiteness before plotting' — plus error-recovery guidance in the troubleshooting section (no/few tiles, missing sections, slow extraction).

5 / 5

Progressive Disclosure

A lean overview with seven clearly signaled, one-level-deep references with descriptive titles ('Slides, pyramid levels, MPP and sample data', 'Tilers, scorers, reports and spatial alignment'), all of which exist as real files in references/, making navigation easy.

5 / 5

Total

19

/

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: concrete capability list, explicit 'Use for' triggers, and explicit routing to a sibling skill for out-of-scope use cases. The only weakness is missing a few natural synonyms and WSI file extensions (e.g., .svs) in the trigger terms.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'Extracts and preprocesses whole-slide histology image tiles', 'WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation' — comprehensively covering the skill's capabilities with no notable gaps.

5 / 5

Completeness

It explicitly answers both what ('Extracts and preprocesses whole-slide histology image tiles with Histolab') and when ('Use for WSI inspection, tissue masks, random/grid/score-based tile extraction...') with concrete trigger phrases, in third-person voice.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('WSI', 'histology', 'tissue masks', 'tile extraction', 'H&E stain normalization'), but a few natural terms users would say are missing, such as file extensions (.svs, .tif), 'digital pathology', or 'whole slide imaging'. The level-5 anchor requires synonyms plus file extensions.

4 / 5

Distinctiveness Conflict Risk

Clear niche (whole-slide histology tiles with Histolab) with explicit de-conflict routing — 'For multiplexed imaging or deep learning inference pipelines, use pathml' — minimizing the risk of triggering for the wrong skill.

5 / 5

Total

19

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/histolab/SKILL.md

Reviewed

Table of Contents

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