deidentify-a-dataset
De-identify selected free-text columns in a local CSV, JSONL, or Parquet dataset with OpenMed and produce a separate redacted dataset plus a PHI-free aggregate summary. Use when an agent must prepare a clinical dataset for analysis or sharing without overwriting the source or exposing cell values in logs.
npx skills add maziyarpanahi/openmed --skill deidentify-a-dataset --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# De-identify a dataset Keep the source local, name the free-text columns explicitly, and write to a different destination. Never infer columns or print source and redacted cell values. ## Procedure 1. Confirm that the input is CSV, JSONL/NDJSON, or Parquet. 2. Confirm which columns contain free text. Do not scan or log values to guess. 3. Choose a policy and language. Prefer `strict_no_leak` when recall is the governing safety requirement. 4. Write to a new path; never overwrite the input. 5. Inspect only `result.summary`, which contains aggregate counts and rates. 6. Validate recall and residual leakage on representative synthetic or approved evaluation fixtures before releasing the output. ## Runnable synthetic example Install the model runtime first with `python -m pip install "openmed[hf]"`. ```python import csv from pathlib import Path from openmed import redact_dataset source = Path("synthetic-notes.csv") destination = Path("synthetic-notes.redacted.csv") with source.open("w", newline="", encoding="utf-8") as handle: writer = csv.DictWriter(handle, fieldnames=["record_id", "note"]) writer.writeheader() writer.writerows( [ { "record_id": "SYNTH-001", "note": ( "Taylor Example
- Procedure
- Runnable synthetic example
- Safety checks
- Repository example
openmed redact-dataset notes.csv \
What does the deidentify-a-dataset skill do?
De-identify selected free-text columns in a local CSV, JSONL, or Parquet dataset with OpenMed and produce a separate redacted dataset plus a PHI-free aggregate summary. Use when an agent must prepare a clinical dataset for analysis or sharing without overwriting the source or exposing cell values in logs.
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill deidentify-a-dataset --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.