Agent skill · data analytics · borghei
people-analytics
People analytics across workforce metrics, predictive modeling, and employee insights. Use when building turnover models, analyzing engagement surveys, running pay equity regressions, or scoring flight risk.
Why this skill is useful
Provides specific methodologies and statistical models for analyzing workforce data that the AI wouldn't reliably generate on its own.
What it needs
About 10k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
People Analytics The agent operates as a senior people analytics partner, translating workforce data into actionable insights using statistical modeling, segmentation analysis, and data governance best practices. Clarify First Before generating the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Business question + success metric — frames the entire analysis (step 1); a vague question yields an unfocused output [ ] Analysis type (descriptive, attrition risk, pay equity, or engagement driver) — selects the method and which script applies [ ] Available data sources + their quality/completeness — determines which method is even possible (step 2) [ ] Segments + privacy threshold (minimum group size) — drives segmentation and the anonymization/aggregation applied Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflow 1. Frame the question -- Clarify the business question with the HR or business stakeholder. Examples: "Why is Sales attrition 2x the company average?" or "Are we paying equitably across gender?" Define the success metric for the analysis. 2. Assess data readiness -- Identify required data sources (HRIS, ATS, survey platform, payroll). Check for completeness, recency, and quality. Flag any gaps before proceeding. 3. Analyze -- Apply the appropriate method from the analytics toolkit (descriptive stats, regression, classification, segmentation). Document assumptions and limitations. 4. Validate findings -- Sense-check results with domain experts (HRBPs, managers). Test for statistical significance and practical significance. Check predictive models for bias across protected groups. 5. Recommend -- Translate findings into 2-3 specific, actionable recommendations with expected impact and cost. 6. Deliver and monitor -- Present insights using the dashboard framework. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/people-analytics