anonymizer

name	anonymizer
description	Use when the user wants to anonymize a text dataset, redact PII, de-identify free-text data, or rewrite text to remove sensitive or inferable identifying information. Produces a runnable Python script that calls the NeMo Anonymizer pipeline (detection → replace or rewrite).
argument-hint	["describe the dataset and how you want it anonymized"]

Before You Start

Do not explore the workspace first. The workflow's data-inspection step shows you what you need.

Goal

Anonymize a text dataset using NeMo Anonymizer in the way the user describes:

$ARGUMENTS

The output is a single runnable Python script that builds an AnonymizerConfig, previews results on a few rows, inspects failures and quality metrics, and (on user approval) runs the full pipeline. The script is the durable artifact — the user keeps it for re-runs, version control, and production.

Workflow

Read workflows/interactive.md and follow it. Anonymization is high-stakes — there is no autopilot mode. Even when the user says "you decide" or "be opinionated", you still ask the minimum questions needed to choose risk_tolerance and phrase privacy_goal, because those are fundamentally the user's calls based on their regulatory and business context.

Rules

• Always preview before running the full pipeline. Preview is cheap; a full run can be expensive and slow.
• If result.failed_records is non-empty after preview, fix that before tweaking strategy. Dropped rows are a model/provider/infra problem (rate limits, auth, etc.), not a config problem. Strategy knobs won't help. See docs/troubleshooting.md "Did the run actually complete cleanly?".
• Ask the user which mode. Briefly describe both: Replace detects entities and replaces each in place (faster, cheaper, keeps shape); Rewrite transforms the full text to also remove inferable identifiers (more expensive, may restructure). Use the data shape as a hint — free-text with implicit identifiers (clinical notes, biographies, depositions) leans Rewrite; structured records / log lines lean Replace — but the user picks.
• For cross-record consistency (same value → same replacement everywhere), use Hash, not Substitute. Substitute is consistent within a row only.
• In Replace mode, default to Substitute if the user hasn't specified a strategy. It's the most general-purpose choice and matches the bulk of production usage.
• Annotate is for inspection, not production. Its output keeps the original entity text and is not privacy-safe. Use it during iteration to confirm detection is working, then switch.
• Always set AnonymizerInput.data_summary, even briefly. It is the single cheapest quality lever and it improves both detection and rewrite.
• Never claim privacy guarantees. Anonymizer is best-effort. Outputs may need human review depending on risk_tolerance. Tell the user this when you finalize.

Usage Tips and Common Pitfalls

• Detect.entity_labels=None (the default) is permissive — the augmenter LLM may invent labels not in DEFAULT_ENTITY_LABELS. Setting an explicit list switches to strict mode where only the listed labels are detected. To add domain labels, extend the default, don't replace it: entity_labels=[*DEFAULT_ENTITY_LABELS, "clinical_facility", ...] (DEFAULT_ENTITY_LABELS is a tuple, so unpack it into a list). Match the snake_case convention of DEFAULT_ENTITY_LABELS.
• GLiNER is zero-shot — entity labels are natural-language concept names (e.g. "clinical_facility", "internal_project_codename"), not codes or enum values. Any concept you can name in English is a label GLiNER can detect.
• Rewrite.instructions is a dead field today — it exists on the model but the rewrite engine never reads it. Do not use it. Put rewriter guidance in privacy_goal.protect / privacy_goal.preserve instead.
• risk_tolerance only applies to Rewrite mode, not Replace.
• PrivacyGoal.protect and .preserve must each be 10–1000 chars and at least 3 words. Be specific (categories, named identifiers, structural facets); avoid generic phrasing like "preserve meaning".
• Validator pool is the only model role with built-in load-spreading. Set entity_validator: [a, b, c] in models.yaml if rate limits drop rows. Other roles (rewriter, evaluator, etc.) are single-alias.

Reference Docs

The agent should consult these as it goes — do not try to enumerate field reference inline:

• docs/concepts/choosing-a-strategy.md — decision tree for picking mode (Replace vs Rewrite), strategy (Substitute / Redact / Annotate / Hash), risk tolerance, privacy goal phrasing, and detection knobs.
• docs/troubleshooting.md — symptom-first guide for diagnosing dropped rows, leakage, low utility, and pipeline failures. Read section by section as symptoms appear, not all at once.
• docs/concepts/detection.md — detection internals (GLiNER threshold semantics, label catalogue, augmenter/validator behavior).
• docs/concepts/models.md — model role configuration, validator pools.

Troubleshooting

Environment-level issues only. Quality and pipeline issues are in docs/troubleshooting.md.

• anonymizer not installed: Tell the user nemo-anonymizer is not in this Python environment (requires Python ≥ 3.11). Ask if they want you to install it (pip install nemo-anonymizer) or do it themselves. Do not install without permission.
• Model aliases not configured: Anonymizer can't run without models.yaml and providers.yaml. Tell the user to set these up — see docs/concepts/models.md. If they don't have a config yet, point them at src/anonymizer/config/default_model_configs/ for the shipped defaults.
• LLM calls failing at preview: Usually an auth issue (missing or invalid API key), a network problem, or a wrong base URL. See docs/troubleshooting.md "Validation passed but preview errors at LLM call".

Output Template

Write a Python script to the current directory. Name it after the dataset (e.g. anonymize_clinical_notes.py, anonymize_support_logs.py). Use this template — fill in the TODO markers, drop unused sections.

"""Anonymize <dataset> using NeMo Anonymizer.

Generated by the anonymizer agent skill.

Usage:
    python <this_script>.py            # preview on 5 rows (fast, cheap)
    python <this_script>.py --full     # run on the full dataset
"""

from __future__ import annotations

import argparse
import sys

from anonymizer import (
    Anonymizer,
    AnonymizerConfig,
    AnonymizerInput,
    DEFAULT_ENTITY_LABELS,
    Detect,
    # Pick what you need:
    # Replace mode:
    Substitute, Redact, Annotate, Hash,
    # Rewrite mode:
    Rewrite, PrivacyGoal,
)


def build_config() -> tuple[AnonymizerInput, AnonymizerConfig]:
    """Single source of truth for what we anonymize and how."""
    data = AnonymizerInput(
        source="TODO: path to .csv / .parquet / .jsonl",
        text_column="TODO: name of the text column",
        data_summary="TODO: one-line description of the data (domain, genre, anything non-obvious)",
    )

    detect = Detect(
        # Add domain labels by *extending* the default, not replacing it.
        # entity_labels=[*DEFAULT_ENTITY_LABELS, "clinical_facility", "diagnosis_code"],
        gliner_threshold=0.3,  # default; lower (0.2) for recall, raise (0.5) for cost savings
    )

    # ---- Pick ONE of the two strategies below ----

    # Replace mode (Substitute | Redact | Annotate | Hash):
    # config = AnonymizerConfig(detect=detect, replace=Substitute(
    #     instructions="TODO: short hint about the domain (e.g. names should remain plausible "
    #                  "for the original cultural context)",
    # ))

    # Rewrite mode (free-text de-identification with inferable-identifier suppression):
    config = AnonymizerConfig(
        detect=detect,
        rewrite=Rewrite(
            privacy_goal=PrivacyGoal(
                protect="TODO: what must not appear in the output, even by inference",
                preserve="TODO: what must be kept so the rewritten text is still useful",
            ),
            risk_tolerance="low",          # minimal | low | moderate | high
            strict_entity_protection=False, # True = force every detected entity into a protective disposition
            max_repair_iterations=3,
        ),
    )
    return data, config


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--full", action="store_true", help="Run on full dataset (default: preview 5 rows)")
    parser.add_argument("--num-records", type=int, default=5, help="Rows to preview (ignored with --full)")
    args = parser.parse_args()

    anonymizer = Anonymizer()
    data, config = build_config()

    if args.full:
        result = anonymizer.run(config=config, data=data)
        out_path = "output.parquet"  # TODO: change path/format (.csv, .jsonl) as needed
        result.dataframe.to_parquet(out_path)
        print(f"Wrote {len(result.dataframe)} rows to {out_path}")
    else:
        result = anonymizer.preview(config=config, data=data, num_records=args.num_records)
        print(f"Previewed {len(result.dataframe)} rows.")

        # Save preview output so you can investigate without re-running.
        # trace_dataframe is a superset of dataframe — it has the user-facing
        # columns plus internal columns (validation decisions, sensitivity
        # dispositions, etc.) that explain why entities were kept, dropped,
        # or rewritten.
        result.trace_dataframe.to_parquet("preview.parquet")
        print("Saved: preview.parquet (load with pd.read_parquet)")

    # Failure-first protocol: dropped rows are infra issues, not strategy issues.
    if result.failed_records:
        print(f"\n⚠️  {len(result.failed_records)} record(s) failed:")
        for fr in result.failed_records[:3]:
            print(f"   - record_id={fr.record_id} step={fr.step} reason={fr.reason}")
        print("\nFix dropped rows before tweaking strategy. See docs/troubleshooting.md.")
        sys.exit(1)

    # Rewrite-mode quality summary (skip for Replace mode).
    if config.rewrite is not None:
        df = result.dataframe
        print(f"\nleakage_mass:   mean={df['leakage_mass'].mean():.3f}  max={df['leakage_mass'].max():.3f}")
        print(f"utility_score:  mean={df['utility_score'].mean():.3f}  min={df['utility_score'].min():.3f}")
        print(f"flagged for review: {int(df['needs_human_review'].sum())} / {len(df)}")


if __name__ == "__main__":
    main()