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diversity-slate-auditor

Audit aggregate candidate slate demographics against reference labor market pools.

Updated May 2, 2026
Search Tags:#dei#diversity-audit#hiring-compliance#slate-audit#recruitment-analytics#labor-market-benchmarking#nyc-ll-144
One-click install
npx skills add https://github.com/marius-bughiu/ooligo --skill diversity-slate-auditor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diversity-slate-auditor
Source: https://github.com/marius-bughiu/ooligo/tree/main/apps/web/public/artifacts/diversity-slate-auditor-skill
Command: npx skills add https://github.com/marius-bughiu/ooligo --skill diversity-slate-auditor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Manually auditing hiring slate diversity composition is time-consuming, prone to unconscious bias, and often lacks defensible, aggregate-only records required for regulatory compliance (such as NYC LL 144) and internal DEI program review. This skill automates the process to produce consistent, auditable outputs without risking per-candidate discrimination.

Core Features & Use Cases

  • Aggregate composition auditing: Compares slate demographic breakdowns to reference labor market pools (e.g., BLS occupational data, industry surveys) using only consented self-reported aggregate data, never processing individual candidate records.
  • Gap surfacing with confidence bands: Flags under- or over-represented demographic dimensions with confidence levels calibrated to slate size, and lists neutral upstream investigation candidates for recruiters and DEI leads to explore.
  • Compliant audit record generation: Emits append-only JSONL audit records with no PII, designed to meet regulatory requirements and support annual DEI reporting and legal discovery.
  • Use case: A DEI lead can use this skill to audit a Q2 senior software engineer slate against BLS software developer demographic data to identify representation gaps, document the audit for compliance, and share neutral investigation leads with the hiring team.

Quick Start

Use the diversity-slate-auditor skill to audit your latest account executive slate against the BLS reference pool and generate a compliant JSONL audit record for your quarterly DEI review.

Frequently Asked Questions about diversity-slate-auditor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit hiring slate diversity against labor market benchmarks?▼

To audit hiring slate diversity, you compare aggregate candidate demographic composition against reference labor market pools like BLS occupational data. This process surfaces representation gaps using only consented self-reported aggregate data, ensuring no per-candidate PII is processed during the slate audit.

Can I generate compliance records for NYC LL 144 using a diversity audit?▼

Yes, diversity audits can generate compliant records for NYC LL 144. The process emits append-only JSONL audit records that exclude all personally identifiable information, designed specifically to meet regulatory requirements and support legal discovery.

How does slate auditing handle candidate privacy and prevent per-candidate discrimination?▼

Slate auditing handles candidate privacy by processing only consented self-reported aggregate data and never processing individual candidate records. It generates audit reports and append-only JSONL records that explicitly exclude all per-candidate personally identifiable information to ensure defensibility.

What is the best way to track internal DEI program representation gaps?▼

The best way to track internal DEI program representation gaps is to audit aggregate candidate slate demographics against labor market benchmarks. This surfaces gaps with confidence bands calibrated to slate size and documents the audit for annual DEI reporting.

Do I need individual candidate demographic data to perform a recruitment analytics audit?▼

No, you do not need individual candidate data for a recruitment analytics audit. The audit processes only consented self-reported aggregate data to identify representation gaps, ensuring no per-candidate personally identifiable information is used to maintain compliance.

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