Protocol Deviation Classifier

Determine whether an incident in a clinical trial is a "major deviation.

This package appears coherent for classifying protocol deviations, but before running it on real clinical data: 1) perform a quick code review of scripts/main.py to ensure there are no network calls or any code that transmits data (look for imports/use of requests, urllib, socket, http.client, ftplib, paramiko, subprocess/os.system, or hardcoded URLs/IPs); 2) verify logging/storage behavior (ensure PHI is not written to shared locations or uploaded); 3) run the included smoke checks (python -m py_compile scripts/main.py and python scripts/main.py --help) and test on synthetic/deidentified examples; 4) confirm the regulatory rationale and recommended actions meet your internal SOPs before relying on outputs for compliance decisions. If you want, I can scan the full scripts/main.py content for network I/O, file writes, or subprocess usage — provide the remainder of the file and I will review it line-by-line.

Type: OpenClaw Skill Name: protocol-deviation-classifier-1 Version: 1.0.1 The skill bundle is a legitimate tool for classifying clinical trial protocol deviations according to GCP and ICH E6 guidelines. The core logic in `scripts/main.py` uses regular expressions and rule-based scoring to assess the impact of deviations on subject safety, data integrity, and scientific validity. The code is well-structured, uses only Python standard libraries, and lacks any indicators of malicious behavior such as data exfiltration, unauthorized network access, or command execution. The instructions in `SKILL.md` are consistent with the tool's stated purpose and do not contain prompt-injection attacks.