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auditing-subgroup-fairness

maziyarpanahi/openmedSkill

Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairness_report. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to surface a documentation gap where subgroup data is missing, or needs equalized-odds-style disparity numbers for a clinical model. Trigger on \"fairness\", \"subgroup\", \"bias audit\", \"disparity\", \"equalized odds\", \"under-protected group\", \"per-group recall\", or \"STANDIN

ScannedSkillSource: GitHub
5,697 stars +43 this week Updated 16d ago License Apache-2.0 Context ≈157 tokens always loaded
77popular

Why this score

Stars19/35
Usage (forks)8/15
Upkeep25/25
License15/15
Owner history10/10

This repo hosts 10 skills, so its 5,697 stars are split between them (about 570 each) for scoring.

Popularity score: 77/100 from public GitHub signals only. It shows how widely used and maintained this is, not how well it works. Not included yet: the fake-star check. The security scan is shown separately and doesn't change the score. How we rank

Security

Scanned Oct 20, 2026 at commit 8440bdac5962, with Cisco's open-source skill-scanner.

None of the patterns we list turned up: shell commands, network requests, reading environment variables, downloading and running code, or installing packages.

A scan lists patterns in the code. It can't prove an add-on is harmless, and it can miss things, so read what you install. How we scan

Alternatives

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