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tooluniverse-immunotherapy-response-prediction

Predict patient response to immune checkpoint inhibitors (ICIs) by integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression. Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment. Use for melanoma/NSCLC/RCC immunotherapy decision support.

68

Quality

82%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

65%

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 thorough, actionable 11-phase workflow with concrete tool calls and explicit scoring math, but it is slightly verbose and, more importantly, its progressive-disclosure references point to bundle files that are missing and it lacks explicit validation checkpoints for the scoring computation.

Suggestions

Add the referenced bundle files (INPUT_REFERENCE.md, SCORING_TABLES.md, REPORT_TEMPLATE.md, TOOLS_REFERENCE.md) or inline their essential content so the signaled references are not dangling.

Insert an explicit validation checkpoint in Phase 10 — e.g. re-check that component scores sum within [0,100] and that confidence matches available-biomarker count — before emitting recommendations.

Trim the biology primer in 'Reasoning Before Searching' and the redundant 'KEY PRINCIPLES' list to the thresholds and priors Claude cannot infer, reducing token weight.

DimensionReasoningScore

Conciseness

The body is dense and mostly high-value (tool calls, thresholds, penalties), but the 'Reasoning Before Searching' biology primer and the 'KEY PRINCIPLES' list restate concepts and workflow steps Claude largely already knows, so it could be tightened.

2 / 3

Actionability

Concrete tool invocations with named parameters (e.g. `fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab')`) plus explicit numeric scoring (STK11 -10, B2M -15) and tier definitions give copy-paste-ready, executable-style guidance.

3 / 3

Workflow Clarity

Eleven phases are clearly sequenced with an overview diagram, but the multi-step scoring computation lacks explicit validate→fix→retry checkpoints; confidence levels flag missing data but no re-validation loop is defined, capping clarity at 2.

2 / 3

Progressive Disclosure

References to INPUT_REFERENCE.md, SCORING_TABLES.md, REPORT_TEMPLATE.md, and TOOLS_REFERENCE.md are clearly signaled one level deep, but none of these bundle files actually exist in the skill directory, so the structure is broken rather than well-formed.

2 / 3

Total

9

/

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.

The description is specific, complete, and distinctive: it names concrete capabilities, integrates a clear 'Use for' trigger with natural cancer-type terms, and occupies a well-defined niche. It is an exemplary concise description.

DimensionReasoningScore

Specificity

Lists multiple concrete actions in third person — 'Predict patient response', 'integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression...', 'Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment' — matching the 'lists multiple specific concrete actions' anchor.

3 / 3

Completeness

Explicitly answers both what (integrate biomarkers, output score, drug recommendations, resistance risk) and when via the explicit trigger 'Use for melanoma/NSCLC/RCC immunotherapy decision support', matching the 'clearly answers both what AND when' anchor.

3 / 3

Trigger Term Quality

Natural trigger terms a user would say are present — 'immunotherapy', 'decision support', and the named cancer types melanoma/NSCLC/RCC — alongside the explicit 'Use for' clause, giving good coverage of natural phrasing rather than pure jargon.

3 / 3

Distinctiveness Conflict Risk

The ICI response-prediction niche with specific biomarker integration and named cancer types is a clear, distinct trigger set unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 7 missing

Warning

Total

15

/

16

Passed

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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