Redacta: An OpenClaw Skill That Pseudonymises Clinical Text Before It Reaches an LLM

If you're building an AI agent that processes clinical text, you're likely worrying about PHI leaking to an external model. Redacta, a new OpenClaw skill, tackles that head-on by pseudonymising identifiers before they leave your environment.

What Redacta does

Instead of simply deleting identifiers (which destroys context), Redacta replaces them with consistent pseudonyms. For example:

John Smith → PERSON_001

Repeated references stay consistent throughout the document, and the mapping can be used later to restore the original text if needed. This preserves useful context while keeping patient data local.

Key features from the source

• Detects names, dates, contact details, and medical record numbers (MRNs).
• Replaces with deterministic pseudonyms like PERSON_001 — not a generic [NAME].
• Mapping allows round-trip restoration of original text.
• Runs entirely locally — no identifiable data sent to an external model.
• Open source and packaged as an OpenClaw skill.
• Passed 1,400 downloads on ClawHub.

Why this matters for agent workflows

When you send clinical text to an LLM API, you're exposing whatever's in that text. Redacta lets you transform the data first, so the model only sees pseudonyms. For developers in healthcare or anywhere that handles sensitive PII/PHI, this is a practical way to add a privacy layer to your agent pipeline.

Redacta is available on ClawHub and the source is on GitHub.

If you've been hand-rolling regex to strip identifiers or relying on external scrubbing services, check it out. The consistent pseudonym approach is a middle ground between full redaction and raw text.