ADA-Predictor: Anti-Drug Antibody Risk Stratification
v1.0.0Predicts the risk of anti-drug antibody development against TNF inhibitors using clinical and genomic data, providing a risk score, tier, and tailored manage...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the provided code and SKILL.md: the script implements the logistic model and Monte Carlo sensitivity analysis described. The only declared dependency (numpy) is used in the code. No unrelated credentials, binaries, or configuration paths are requested.
Instruction Scope
SKILL.md instructs running the local Python script and documents the model and usage. The instructions do not request reading system files, environment variables, or sending data to external endpoints; runtime behavior is limited to local computation and printing.
Install Mechanism
There is no install spec (instruction-only + included script). The dependency list (numpy>=1.24) is proportionate and expected; no downloads, URL-based installs, or archive extraction are present.
Credentials
The skill requires no environment variables, credentials, or config paths. The code does not access os.environ or attempt to load external secrets—requested privileges are minimal and appropriate.
Persistence & Privilege
The skill is not always-included and does not modify other skills or system settings. It runs locally and does not persist credentials or alter agent configuration.
Assessment
This package appears coherent and self-contained: it runs a local risk model and prints results and sensitivity estimates. Before using in clinical workflows, (1) verify the model's external validity on your patient population and check the cited references, (2) run the script locally in a controlled environment (ensure numpy is installed), (3) avoid supplying identifiable patient data unless you have appropriate privacy safeguards, and (4) treat outputs as decision-support only—do not replace clinical judgment or regulatory requirements. If you need automated integrations (logging, EHR input/output), review and sandbox any added code for data handling and external network activity.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.