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structured-risk-evaluation

structured-risk-evaluation

Structured tabular de-identification, release-risk evaluation, and compliance evidence.

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Documentation de la Skill

GitHub

name: structured-risk-evaluation description: "Structured tabular de-identification, release-risk evaluation, and compliance evidence." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Structured Risk Evaluation

Use this sub-skill for synthetic or approved tabular release workflows that need quasi-identifier discovery, k-anonymity, l-diversity, t-closeness, differential privacy aggregates, membership risk, anonymized output validation, and compliance evidence.

Route elsewhere when the task is about

  • Free-text PHI or PII redaction → deidentification-privacy.
  • Clinical entity extraction or grounding → clinical-extraction-grounding.
  • Service deployment, transport, or adapter wiring → interoperability-serving.

Core surfaces

  • openmed.structured
  • openmed.risk
  • openmed.eval
  • openmed.compliance
  • CLI families: risk, audit, compliance, benchmark, eval, gates, calibrate

Safe workflow

  1. Discover candidate direct identifiers, quasi-identifiers, sensitive columns, and the privacy unit with scan_table or profile_structured_table.
  2. Review the policy explicitly; do not infer threshold values.
  3. Assess the raw table with assess_release, analyze_k_anonymity, assess_population_risk, or the DP and membership helpers that match the task.
  4. Anonymize with anonymize_release or anonymize_table.
  5. Re-read the materialized release and validate with validate_released_output.
  6. Package aggregate-only audit/compliance evidence and, when needed, feed it into release gates and dashboards.
  7. Keep all examples synthetic. Never depend on DUA-restricted rows, raw PHI, or deployment-time services.

Bundled references

  • references/risk-eval-workflows.md
  • references/api-reference.md
  • references/troubleshooting.md

Bundled helper script

  • scripts/structured_release_check.py

Output expectations

  • Aggregate JSON summaries only.
  • Evidence files may include Markdown and JSON, but never raw cell values.
  • Release artifacts should be materialized locally, reread, and validated before review or gating.
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