Conciseness

Mostly efficient with dense, actionable phrasing (e.g., scoring tables, point penalties) and minimal concept re-education, though the biology refresher in 'Reasoning Before Searching' and the principles list add some length that could be trimmed.

Actionability

Highly executable: specific tool calls with parameters (e.g., `fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab')`), exact scoring rules (STK11 -10, POLE +10), a concrete score formula, and parameter gotchas for every tool.

Workflow Clarity

Clear 11-phase sequence with explicit scoring integration, but validation checkpoints are light — the 'COMPUTE, DON'T DESCRIBE' and 'LOOK UP DON'T GUESS' guidance substitute for explicit validate-then-proceed feedback loops.

Progressive Disclosure

The body references INPUT_REFERENCE.md, SCORING_TABLES.md, REPORT_TEMPLATE.md, and TOOLS_REFERENCE.md as one-level-deep links, but none of these bundle files actually exist in the skill directory, so the disclosure structure is broken/unverifiable.

Specificity

Multiple concrete actions named: 'integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression' and 'Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment' — comprehensive and concrete.

Completeness

Explicitly states what it does ('Predict patient response... by integrating...') and when to use it ('Use for melanoma/NSCLC/RCC immunotherapy decision support') with concrete trigger context.

Trigger Term Quality

Strong clinical terms ('melanoma/NSCLC/RCC immunotherapy decision support', 'patient response to immune checkpoint inhibitors') but lacks common synonyms or casual phrasing a user might naturally say; mostly domain jargon.

Distinctiveness Conflict Risk

Highly niche — multi-biomarker ICI response prediction for specific cancer types — with minimal overlap risk against generic oncology or bioinformatics skills.