Clinical Data Cleaner
ReviewAudited by ClawScan on May 1, 2026.
Overview
The skill appears to be a local clinical-data cleaning helper with no evidenced exfiltration or credential use, but its outputs can alter regulated clinical datasets and should be reviewed carefully.
Use this skill only in a controlled clinical-data environment. Keep raw files unchanged, run cleaning on copies, choose conservative options such as flagging outliers, protect any PHI or trial identifiers in outputs, pin dependencies, and require independent QC/regulatory review before relying on results for submissions.
Findings (3)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
A cleaned file could omit records or change values, affecting downstream analysis or regulatory submissions.
The tool can remove rows or cap values in clinical datasets. This is directly related to data cleaning, but it can materially alter regulated trial data if used without review.
Strategies:\n - drop: Remove rows with any missing values ... Actions:\n - remove: Remove outlier rows\n - cap: Cap values at thresholds
Run on copies of source data, prefer flagging over removal for regulated workflows, and have a statistician/QA reviewer inspect the audit trail and output before use.
Users might over-trust the generated reports as regulatory certification when they still require independent validation and compliance review.
The reference material includes strong compliance wording. Other artifacts also advise validation and review, so this is best treated as a caution rather than deception.
Audit Trail:\n - Cleaning reports: 3 files\n - All actions documented\n - 21 CFR Part 11 compliant
Treat the reports as supporting documentation only; validate with accepted clinical data tools and follow organizational regulatory/QA procedures.
Installing later package versions could produce different behavior or introduce package-level supply-chain risk.
The Python dependencies are listed without version pins. They are expected for this data-cleaning purpose, but unpinned dependencies can change over time.
numpy\npandas\nscipy
Use a trusted Python environment, pin dependency versions, and keep a reproducible environment for regulated clinical work.