Redacta

Redacta pseudonymises medical documents — replacing patient identifiers (NHS numbers, dates of birth, postcodes, phone numbers, hospital numbers) with labell...

This skill appears coherent and lightweight, but check the following before using on real patient data: 1) Model/data handling: the SKILL.md says processing is "local" but actual text may be sent to whatever language model you use—confirm your model provider's data processing and retention policies or run models on-prem if required. 2) Clinician/institution names: the default preserves clinician and hospital names; if your policy requires removing staff or institutional identifiers, instruct the agent to redact them or change the rule. 3) Review outputs: the skill admits it cannot guarantee 100% detection — always manually review pseudonymised text and the redaction report before sharing. 4) Test first: run the skill on representative non-sensitive examples to validate regex/context rules (especially NHS check-digit behaviour and date-context decisions). 5) Scope limits: v1 is text-only (no PDFs/images). If you need stronger guarantees or audit logging, consider integrating with a vetted de-identification pipeline rather than relying solely on instruction-driven redaction.

Type: OpenClaw Skill Name: redacta Version: 1.0.0 The OpenClaw skill 'redacta' is designed to pseudonymise medical documents by replacing patient identifiers with tokens. The `SKILL.md` instructions explicitly state that the skill does not store or transmit patient data, and it instructs the AI agent to never output original patient identifiers. There are no instructions for data exfiltration, unauthorized command execution, network calls beyond the stated purpose, or any form of prompt injection against the agent to perform malicious actions. The skill's behavior is clearly aligned with its stated privacy-preserving purpose.