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employee-performance-analytics-hr

Analyze employee performance data with SQL KPI aggregation and Python visualization dashboards.

5|1|Updated May 16, 2026
Search Tags:#data visualization#sqlite#sql#pandas#kpi dashboard#hr analytics#employee performance
One-click install
npx skills add https://github.com/reason-machines/data-skills --skill employee-performance-analytics-hr-reason-machines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: employee-performance-analytics-hr
Source: https://github.com/reason-machines/data-skills/tree/main/skills/employee-performance-analytics-hr
Command: npx skills add https://github.com/reason-machines/data-skills --skill employee-performance-analytics-hr-reason-machines

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, seaborn, numpy.

What problem does it solve? HR teams often have raw employee activity data in CSV files but no structured way to compute departmental KPIs, individual efficiency metrics, or productivity trends. This Skill turns that raw data into aggregated SQL views, CSV reports, and charts without building a custom analytics pipeline from scratch. ## Core Features & Use Cases - SQL KPI Aggregation: Creates SQLite views for department KPIs, employee summaries, and daily productivity using queries on tasks, hours, ratings, and absences. - Automated Report Generation: Exports department_kpis.csv and performance_summary.csv from the analytical views. - Visualization Pipeline: Generates matplotlib/seaborn charts including department rating bars, performance-vs-hours scatter plots, and efficiency distribution histograms. - Use Case: An HR analyst loads a CSV of daily employee activity, runs the pipeline, and receives ranked department KPIs, top-performer lists, and trend charts for a quarterly review. ## Quick Start Load my employees.csv into SQLite and generate department KPI reports and performance charts in an outputs folder.

Frequently Asked Questions about employee-performance-analytics-hr

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

FAQPage Schema
How do I analyze employee performance data with SQL and Python?▼

Load the employee CSV into SQLite with create_db.py, then run analyze_performance.py with the queries.sql file. It creates views for department KPIs, employee summaries, and daily productivity, then exports CSV reports and charts.

What columns does the employee CSV data need?▼

The CSV must include employee_id, name, department, role, date, tasks_completed, hours_worked, rating, projects, and absences. Missing columns cause validation errors, so verify the schema with pandas before loading into SQLite.

How do I calculate employee efficiency in SQL?▼

Divide total tasks_completed by total hours_worked per employee, as shown in the employee_summary view. Guard against division by zero by checking hours_worked is greater than zero before computing the ratio.

Can I use this with a database other than SQLite?▼

The provided scripts target SQLite via Python's built-in sqlite3 module. To use PostgreSQL or MySQL, you would need to adapt the connection code and adjust SQLite-specific functions like strftime in the queries.

Why does my efficiency calculation return null or error values?▼

This happens when an employee has zero total hours worked, causing division by zero. Apply a conditional check so efficiency is only computed when total_hours is greater than zero, otherwise set it to null.

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