histolab

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

Quality

85%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

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

A well-structured, highly actionable skill body with good progressive disclosure and real validation checkpoints. Its main weakness is token-economy: Quick Start/Workflow 1 duplication and dual reference summaries could be tightened.

Suggestions

Remove the near-verbatim duplication between Quick Start and Workflow 1 — have Quick Start point to Workflow 1 or keep only a minimal snippet in one place.

Drop the bullet-list reference summaries duplicated in the Resources section; link to each reference file once (in its per-capability section) and let the Resources index list only paths.

Tighten or remove the 'Overview' paragraph's generic framing ('Histolab is a Python library...') since Claude can infer this from the routing boundary and imports.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with list-driven prose and executable code, but Quick Start is repeated nearly verbatim in Workflow 1 and the five reference summaries are duplicated between the per-capability sections and the Resources index, which is more than minor padding.	3 / 5
Actionability	Copy-paste-ready examples with real imports, parameters, and defaults cover the three tilers, masks, filters, and visualization comprehensively, matching the fully-executable anchor-5 example; the few undefined-variable cases are minor.	5 / 5
Workflow Clarity	Clear sequences with an explicit preview-before-extract checkpoint and a Troubleshooting recovery section; not a 5 because validation is informal (thumbnails/preview) rather than a strict pass/fail gate, and not a 3 because real checkpoints are present for this batch operation.	4 / 5
Progressive Disclosure	Overview-style SKILL.md with five well-signaled, one-level-deep references (all verified real files) and a Resources index; content is appropriately split across files despite minor duplicate signposting.	5 / 5
	Total	17 / 20 Passed

Description

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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 specific, well-triggered description that answers both what and when and draws a clear boundary against adjacent skills. The only gap is missing file-extension synonyms like .svs in the trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'tile extraction', 'preprocessing', 'tissue detection', 'stain normalization for H&E images' — giving comprehensive coverage of the library's surface, matching the anchor-5 example.	5 / 5
Completeness	Explicitly answers both what (tile extraction, preprocessing, tissue detection, stain normalization) and when ('Use for basic slide processing...', 'Best for simple pipelines, dataset preparation'), plus a routing cue to pathml — matching the anchor-5 example with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural terms (WSI, tile extraction, tissue detection, H&E, stain normalization, dataset preparation) but missing file extensions (.svs) and a few synonyms, so it sits at good-but-not-comprehensive keyword coverage rather than a 5.	4 / 5
Distinctiveness Conflict Risk	Clear niche (lightweight histolab WSI tiling) with an explicit boundary statement routing advanced/multiplexed/DL work to pathml, giving distinct triggers and minimal conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

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

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (680 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	14 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/histolab/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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