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tooluniverse-cancer-classification

Translate free-text tumor descriptions to OncoTree codes and resolve cancer subtypes/tissue hierarchy. Cross-references UMLS/NCI vocabularies. Use for standardizing cancer-type nomenclature in EHR free-text, building cohorts in OncoKB or GDC, mapping tumor-board notes to ontology codes, and ensuring consistent terminology across cancer-genomics pipelines.

72

Quality

88%

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Low

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SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

Highly actionable and clearly sequenced with validation checkpoints and fallbacks, but the body is long and bundles clinical teaching that could be tightened or moved into referenced files to improve token efficiency and progressive disclosure.

Suggestions

Trim the clinical teaching sections (histological vs molecular classification, staging vs grading, biomarker interpretation strategy) to the tool-use guidance Claude needs, or move them into a referenced reference file to improve conciseness.

Add a references/ bundle file (e.g., BIOMARKERS.md or EVIDENCE.md) and link to it one level deep to offload the dense interpretation material and raise progressive disclosure.

Move the verified codes table and biomarker examples closer to the relevant workflow phases so the core tool-use loop stays compact.

DimensionReasoningScore

Conciseness

Mostly efficient with tool tables and terse code blocks, but the ~200-line body teaches substantial clinical oncology (histological vs molecular classification, staging vs grading, biomarker interpretation, evidence grading) that pads beyond what Claude needs to drive the tools.

2 / 3

Actionability

Fully executable tool calls with real parameters and response-field annotations ('OncoTree_search(query="breast cancer")', 'OncoKB_annotate_variant(gene="EGFR", variant="L858R", tumor_type="LUAD")') plus a verified codes table — copy-paste ready.

3 / 3

Workflow Clarity

A clear 4-phase sequence with an explicit validation checkpoint ('Always validate via OncoTree_get_type before using in downstream tools') and fallback chains for 404/unrecognized-code recovery.

3 / 3

Progressive Disclosure

Well-sectioned single file, but no bundle files exist and the dense biomarker/evidence-grading material that could be offloaded is all inline rather than split into one-level-deep references.

2 / 3

Total

10

/

12

Passed

Description

100%

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 pairs concrete capabilities with an explicit, trigger-rich 'Use for...' clause, giving it full marks on specificity, trigger quality, completeness, and distinctiveness.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Translate free-text tumor descriptions to OncoTree codes and resolve cancer subtypes/tissue hierarchy' and 'Cross-references UMLS/NCI vocabularies' — rather than vague language.

3 / 3

Completeness

Explicitly answers both 'what' (translate, resolve, cross-reference) and 'when' via an explicit 'Use for...' clause with concrete triggers.

3 / 3

Trigger Term Quality

Strong coverage of natural terms a researcher would say: 'EHR free-text', 'building cohorts in OncoKB or GDC', 'mapping tumor-board notes to ontology codes'.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (OncoTree cancer classification with UMLS/NCI cross-refs) with distinct, domain-specific triggers unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

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