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pathology-roi-selector

Use pathology roi selector for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Quality

46%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/pathology-roi-selector/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

35%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.

The body is a templated shell: extensive process-discipline boilerplate and repeated description text, but almost no substantive guidance on how to perform pathology ROI selection, and its only executable reference (scripts/main.py) is missing from the bundle. Structure exists, but there is nothing actionable at its core.

Suggestions

Ship the referenced scripts/main.py (or remove all references to it) and document its real parameters — input WSI path, tissue type, magnification — with concrete values and example invocations.

Cut the verbatim description repetitions, the circular 'See ## X above' pointers, and the generic template sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that carry no domain information.

Replace the generic 5-step communication workflow with the actual ROI-selection procedure: how tissue/tumor regions are detected, thresholds used, and how output coordinates and crops are produced and validated.

DimensionReasoningScore

Conciseness

The body is noticeably padded: the description sentence is repeated verbatim in 'When to Use' and 'Key Features', circular no-op pointers appear ('See ## Prerequisites above', 'See ## Workflow above'), and generic template sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status with a stale 'Next Review Date: 2026-03-06') add no domain value.

2 / 5

Actionability

The only concrete commands are 'python -m py_compile scripts/main.py' and 'python scripts/main.py --help', yet scripts/main.py does not exist in the bundle; there is no actual ROI-selection guidance — no thresholds, tissue-detection method, or real parameter values, and 'Edit the in-file CONFIG block' is hypothetical.

2 / 5

Workflow Clarity

A 5-step Workflow with a Quick Check (py_compile) and an Error Handling/fallback section provides a sequenced path with some checkpoints, but every step is generic process discipline ('confirm the user objective… return a structured result') rather than the actual domain operation, and validation is not tied to real execution.

3 / 5

Progressive Disclosure

Section headers give the body reasonable structure, but the sole bundle reference (scripts/main.py) is dangling — the file does not exist — and cross-references point to inline sections while boilerplate that serves no navigational purpose is inlined.

3 / 5

Total

10

/

20

Passed

Description

33%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.

The description names a niche domain but describes process qualities instead of capabilities, and its trigger language is generic enough to conflict with most data-analysis skills. It omits the skill's actual function (ROI detection in whole-slide images) and the vocabulary users would naturally employ.

Suggestions

State the concrete function first, e.g. 'Detects and extracts tumor-rich regions of interest (ROIs) from whole-slide pathology images (WSI, .svs) for AI training data'.

Replace generic process language ('structured execution, explicit assumptions') with domain-specific triggers users would actually say: 'whole slide image', 'WSI', 'tissue detection', 'tumor regions'.

Add an explicit 'Use when…' clause naming the pathology/imaging context so the skill does not fire for general data-analysis tasks.

DimensionReasoningScore

Specificity

The description names the domain ('pathology roi selector') but the stated actions — 'structured execution, explicit assumptions, and clear output boundaries' — are generic process qualities, not concrete capabilities; it never states what the skill actually does (selecting regions of interest in whole-slide images).

2 / 5

Completeness

A 'when'-equivalent clause is present ('for data analysis workflows that need…'), so it is not anchor 2's missing 'when', but the 'what' is vague — process discipline rather than the ROI-selection function — so it falls short of anchor 4's explicit both-what-and-when.

3 / 5

Trigger Term Quality

The only keywords are the skill's own name and the generic phrase 'data analysis workflows'; natural user phrases like 'whole slide image', 'WSI', 'tissue', 'tumor regions', or '.svs' are all missing.

2 / 5

Distinctiveness Conflict Risk

The trigger language 'data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries' would match nearly any analysis skill, creating high overlap risk despite the niche domain name.

2 / 5

Total

9

/

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

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 9 missing

Warning

Total

14

/

16

Passed

Repository

aipoch/medical-research-skills

Path

scientific-skills/Data Analysis/pathology-roi-selector/SKILL.md

Reviewed

Table of Contents

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