Lists multiple specific concrete actions: 'Predict patient-specific neoantigen candidate peptides', 'high immunogenicity', 'based on HLA typing and tumor mutation profiles', 'tumor immunotherapy target screening'. These are precise, domain-specific capabilities.

Clearly answers 'what' (predict neoantigen candidates based on HLA/mutation data for immunotherapy screening), but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied through the purpose statement.

Contains relevant technical keywords like 'neoantigen', 'HLA typing', 'tumor mutation', 'immunotherapy', 'peptides', but these are highly specialized terms. Missing common variations or simpler terms users might say like 'cancer vaccine targets', 'tumor antigens', or 'personalized cancer therapy'.

Highly specialized niche in computational immunology/oncology. The combination of 'neoantigen', 'HLA typing', 'tumor mutation profiles', and 'immunotherapy' creates a distinct fingerprint unlikely to conflict with other skills.

The skill contains some unnecessary verbosity, particularly in explaining what neoantigens are (Claude knows this), and the response template section is overly prescriptive. However, the core technical content is reasonably efficient.

Provides fully executable command-line examples, Python API code, concrete input format specifications with examples, and specific scoring thresholds. The code is copy-paste ready and includes real parameter values.

Clear numbered workflow steps with explicit validation gates, fallback behavior with specific failure handling, and input validation as a hard gate before processing. The fallback section explicitly states what can still be completed when parts fail.

Content is reasonably organized with clear sections, but the skill is monolithic with no references to external files for detailed content like algorithm documentation or extended examples. The response template section could be extracted to a separate reference.