Rare Disease HPO Mapper

Map patient symptoms to Human Phenotype Ontology terms for gene diagnosis.

What to consider before installing or running this skill: - Functionality: The script contains a tiny, hardcoded HPO term set. If you need comprehensive mapping, this implementation is insufficient — review and extend the HPO dictionary or integrate an authoritative ontology source. - Documentation mismatch: SKILL.md mentions a CONFIG block and file input/output, but scripts/main.py only accepts a --symptoms CLI argument and prints results. Confirm intended behavior with the skill author before using in automated workflows. - Data sensitivity: This tool processes clinical symptom text (protected health information). Run it only in a secure, compliant environment; do not send PHI to untrusted services. Ensure workspace access and logs are controlled. - Input validation & CSV/command injection: The script trims and matches strings but does not sanitize inputs beyond that. If you integrate it into pipelines that write outputs to files, sanitize and escape content to avoid downstream CSV/formula injection or logging of sensitive text. - Dependencies: requirements.txt incorrectly lists 'difflib' (stdlib). Remove or correct the requirements file to avoid misleading users. - Recommended improvements: add explicit file input/output options (if desired), robust input validation, larger HPO backing data or an API with documented access controls, and unit tests. If you need privacy guarantees, run inside a sandboxed environment and confirm no network calls occur. Overall: the skill appears internally consistent and not malicious, but it is limited and the documentation should be clarified before trusting it in production clinical workflows.

Type: OpenClaw Skill Name: rare-disease-hpo-mapper Version: 1.0.0 The skill is a straightforward clinical tool designed to map symptoms to Human Phenotype Ontology (HPO) terms using a hardcoded dictionary and fuzzy matching via the standard 'difflib' library. The Python script (scripts/main.py) contains no network calls, file system writes, or dangerous execution sinks like eval() or os.system(). The instructions in SKILL.md are well-structured, emphasizing input validation and scope adherence without any signs of prompt injection or malicious intent.