Redacta

Pseudonymises medical and clinical documents by replacing patient identifiers with labelled tokens (e.g. [PATIENT_NAME_1], [NHS_NUMBER_1], [DATE_OF_BIRTH_1]) so the text can be safely processed by AI or shared, with clinical meaning intact. Combines a deterministic pattern layer (NHS numbers with Modulus-11 validation, UK National Insurance numbers, dates of birth, UK postcodes, phone numbers, emails, hospital/MRN numbers) with contextual reasoning for patient names, postal addresses and identifying ages, then returns the redacted document plus a redaction report. Use when the user wants to re

Install

skills CLI (Claude Code, Cursor, Codex and more)
npx skills add nickjlamb/redacta --skill redacta
Manual install
git clone --depth 1 https://github.com/nickjlamb/redacta.git /tmp/redacta
mkdir -p ~/.claude/skills
cp -r /tmp/redacta/. ~/.claude/skills/redacta

Personal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.

From the SKILL.md

# Redacta

Pseudonymise medical text before it is processed by AI or shared: replace patient
identifiers with labelled tokens (`[PATIENT_NAME_1]`, `[NHS_NUMBER_1]`,
`[DATE_OF_BIRTH_1]`, ...) while leaving the clinical meaning untouched. Return the
redacted document plus a redaction report.

Redacta works in two layers:

- **Layer 1 — patterns (deterministic).** Fixed-format identifiers, matched by a
  bundled script: NHS numbers (Modulus-11 validated), UK National Insurance
  numbers, dates of birth, UK postcodes, phone numbers, emails, hospital/MRN
  numbers. (US SSN and ZIP codes are also handled.)
- **Layer 2 — reasoning (your judgement).** Identifiers that do not follow a fixed
  pattern: patient names, postal addresses and identifying ages. This is where you
  read context and tell a patient apart from the clinician treating them.

## Workflow

Copy this checklist and tick items off as you go:

```
Redaction progress:
- [ ] 1. Save the source text to a file
- [ ] 2. Run the pattern layer (scripts/redact_structured.py)
- [ ] 3. Apply the reasoning layer (names, addresses, ages)
- [ ] 4. Assemble the pseudonymised document (formatting preserved)
- [ ] 5. Self-check the output for residual identifiers
- [ ] 6. Write the redaction report
- [ ] 7. Add the limits note
```

### 1–2. Pattern layer

Write the user's text **verbatim** to a temp file, then run the script (execute
it — do not read it into context):
Read the full skill on GitHub

Details

Skill name
redacta
License
MIT-0
Repo updated
6 October 2026

FAQ

What does the Redacta skill do?

Pseudonymises medical and clinical documents by replacing patient identifiers with labelled tokens (e.g. [PATIENT_NAME_1], [NHS_NUMBER_1], [DATE_OF_BIRTH_1]) so the text can be safely processed by AI or shared, with clinical meaning intact. Combines a deterministic pattern layer (NHS numbers with Modulus-11 validation, UK National Insurance numbers, dates of birth, UK postcodes, phone numbers, emails, hospital/MRN numbers) with contextual reasoning for patient names, postal addresses and identifying ages, then returns the redacted document plus a redaction report. Use when the user wants to re

How do I install the Redacta skill?

skills CLI (Claude Code, Cursor, Codex and more): npx skills add nickjlamb/redacta --skill redacta. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.

Is Redacta popular?

It lives in nickjlamb/redacta, which has 8 GitHub stars and 0 forks.

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