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Grok Bot+18 agentsPeople Analytics Pro
Turn reliable, privacy-aware HR data into clear workforce metrics, trend explanations, and decisions with transparent definitions and human review.
$9.99
People Analytics Pro
Example session with this skill installed
Review this quarterly HR dataset for workforce trends. First validate definitions and missingness. Summarize voluntary attrition, internal mobility, and training participation by quarter and department, flag any small groups that should be suppressed, separate observations from possible explanations, and propose follow-up questions. Do not infer why a person left or make individual employment recommendations.
Dataset notes: voluntary exits 18/420 in Q1 and 21/430 in Q2; internal moves 12/420 and 17/430; training participants 130/420 and 155/430. Department-level counts are available but two departments have fewer than 10 employees. Definitions were stable except training participation in Q2 includes one newly added course.
- Read your context and instructions
- Compiled the people analytics pro
Quarterly Workforce Analytics Brief
Executive summary
Across the supplied totals, voluntary exits rose from 18 of 420 employees in Q1 (4.3%) to 21 of 430 in Q2 (4.9%). Internal moves increased from 12/420 (2.9%) to 17/430 (4.0%). Reported training participation rose from 130/420 (31.0%) to 155/430 (36.0%), but Q2 includes a newly added course, so the participation comparison is not fully like-for-like. These are descriptive changes; the data does not establish causes.
Metric checks
| Measure | Q1 | Q2 | Interpretation |
|---|---|---|---|
| Voluntary exits / headcount | 4.3% | 4.9% | +0.6 percentage points; check consistent exit definition and average vs point-in-time headcount |
| Internal moves / headcount | 2.9% | 4.0% | +1.1 points; confirm whether transfers are counted once and whether denominator is comparable |
| Training participants / headcount | 31.0% | 36.0% | +5.0 points; course catalog changed in Q2, limiting comparability |
Privacy and quality flags
- Suppress or combine department-level results for groups under the organization’s minimum reporting threshold; two departments are below 10 employees.
- Confirm whether each denominator is quarter-end or average headcount and whether workers on leave or contractors are included.
- Check data completeness, duplicate records, effective dates, and consistent reason/status coding.
- Separate voluntary exits from all exits and state the exact population used.
Follow-up questions
- Are workforce definitions and periods consistent across quarters?
- Does training participation rise when calculated against a stable course set?
- Are trends concentrated in a sufficiently large group to report safely?
- What context should HR review before forming hypotheses?
Limits
Small-group details are intentionally not shown. Aggregate changes do not explain individual behavior or prove that one event caused another. HR should validate the data and context before action.
Connects securely to your tools. The creator never sees your data.
About this skill
People Analytics Pro
Turn HR data into careful, decision-ready workforce analysis. This skill helps teams define meaningful metrics, validate source quality, interpret trends, and communicate findings with their limits visible. It prioritizes aggregate insight and responsible data handling.
Deliverables
- A question-to-metric plan with definitions, populations, periods, and denominators
- A data-quality review covering completeness, duplicates, changed definitions, and known gaps
- Cohort and trend summaries with sample-size and comparability cautions
- Evidence-linked explanations that separate observed patterns from hypotheses
- Options for follow-up analysis and accountable human owners
Workflow
- Clarify the workforce question and the decision it may inform.
- Inventory authorized sources, access rules, refresh dates, and data limitations.
- Define measures consistently before comparing periods or groups.
- Check missingness, coverage, small cohorts, selection effects, and changes in collection.
- Analyze aggregate patterns, state uncertainty, and distinguish association from causation.
- Present proportionate actions for HR and leaders to validate.
Responsible use
Use the minimum authorized data needed. Prefer aggregate reporting and suppress or combine small groups under organizational policy. Do not infer health, protected characteristics, or other sensitive attributes; do not create individual risk scores or recommend employment outcomes. Human reviewers must assess context, fairness, legal requirements, and data access before acting.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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