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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.

65

Quality

82%

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SKILL.md
Quality
Evals
Security

Quality

Content

68%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.

A highly actionable, well-structured skill body with excellent executable examples and real reference files, weakened by clear redundancy — reference catalogs repeated between sections and the Resources section — and by the absence of explicit post-extraction validation in the batch multi-slide workflow, which caps workflow clarity. Trimming the duplicated reference listings and consolidating repeated examples would move it toward top marks.

Suggestions

Add an explicit validation step to the batch Workflow 4 (e.g., count extracted tiles per slide with `len(list(output_dir.glob("*.png")))` and re-run or flag slides yielding zero tiles), which would lift workflow clarity past the batch-operation cap.

Delete the per-section "Reference: references/x.md contains comprehensive documentation on:" bullet lists and keep a single Resources section (or vice versa) — the duplication costs ~100 lines with no new information.

Consolidate the near-identical RandomTiler examples (Quick Start, Workflow 1, Workflow 5) into one canonical example and vary the others, and trim the 60-line Resources catalog to one-line-per-file pointers since each section already signals its reference.

DimensionReasoningScore

Conciseness

The body is mostly dense, useful code and parameters, but it is noticeably padded: every capability section repeats a "Reference: references/x.md contains comprehensive documentation on:" bullet list that is duplicated nearly verbatim again in the 60-line Resources section, and the RandomTiler example appears three times (Quick Start, Workflow 1, Workflow 5's tiler). Not 2 because the padding is redundancy rather than explanations of concepts Claude already knows; not 4 because the duplication is substantial and could be cut by ~150 lines without losing information.

3 / 5

Actionability

Code throughout is complete and copy-paste executable with real imports, concrete parameters (`tile_size=(512, 512)`, `tissue_percent=80.0`, `seed=42`, `report_path="tiles_report.csv"`), and examples cover the common cases (random/grid/scored extraction, custom filter chains, multi-slide loops). Not 4: there are no pseudocode gaps and the examples span the library's typical usage.

5 / 5

Workflow Clarity

The five workflows are clearly sequenced (load → inspect → preview → extract) and "Always preview before extracting" plus the Troubleshooting section give real checkpoints and error recovery. However, per the rubric's batch-operation cap, Workflow 4 (multi-slide batch processing) has no post-extraction validation — no tile-count check or output verification per slide — so workflow clarity is capped at 3 despite the good sequencing.

3 / 5

Progressive Disclosure

Good structure: overview, quick start, five capability sections each signaling a one-level-deep reference file (all five exist in the bundle), workflows, best practices, and troubleshooting. Not 5: the ~680-line main body inlines substantial detail (full multi-workflow code, per-section reference bullet lists, and a duplicated Resources catalog) that belongs in the already-present reference files, and the reference listings are stated twice rather than signaled once cleanly.

4 / 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 that states concrete capabilities, gives explicit use-when guidance with a best-for clause, and cleanly boundaries itself against a sibling skill. Its only weakness is keyword coverage that stays within expert vocabulary (WSI, H&E) rather than including the natural phrases and file extensions a broader user base would say.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — "WSI tile extraction and preprocessing", "slide processing tissue detection, tile extraction, stain normalization for H&E images" — with comprehensive coverage of the library's scope. Not 4: the action list covers the library's full capability surface rather than having minor coverage gaps.

5 / 5

Completeness

Explicitly answers both questions: what ("Lightweight WSI tile extraction and preprocessing... stain normalization for H&E images") and when ("Use for basic slide processing... Best for simple pipelines, dataset preparation, quick tile-based analysis"), plus an explicit boundary ("For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml"). Not 4: the 'when' is explicit with multiple concrete trigger scenarios, not merely present with room to be more specific.

5 / 5

Trigger Term Quality

Good natural-term coverage: "tile extraction", "tissue detection", "stain normalization", "H&E", "dataset preparation", "tile-based analysis". Not 5: it leans on the jargon abbreviation "WSI" without the natural phrase "whole slide images", and omits common variations like "histopathology", "digital pathology", or file extensions (.svs, .tif) that users would say.

4 / 5

Distinctiveness Conflict Risk

Clear niche (WSI digital pathology preprocessing) with distinct domain-specific triggers, and it actively reduces conflict risk by routing adjacent needs ("spatial proteomics, multiplexed imaging, or deep learning pipelines") to a named alternative skill. Minimal overlap risk.

5 / 5

Total

19

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (684 lines); consider splitting into references/ and linking

Warning

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

Total

13

/

16

Passed

Repository

synthetic-sciences/openscience

Path

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

Reviewed

Table of Contents

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