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Staff Schedule Optimizer

Build a labor-efficient weekly schedule that aligns staffing levels with forecasted demand, respects labor-law constraints, balances employee preferences, and keeps labor cost within target percentage of projected revenue.

Saves ~1 hr/scheduleintermediate Claude ยท ChatGPT ยท Gemini

๐Ÿ“… Staff Schedule Optimizer

Purpose

Build a labor-efficient weekly schedule that aligns staffing levels with forecasted demand, respects labor-law constraints, balances employee preferences, and keeps labor cost within target percentage of projected revenue.

When to Use

Use this skill when building the upcoming week's schedule โ€” ideally after running the Demand Forecast Briefing so projected covers are available. Also useful when re-balancing after call-outs or when onboarding new hires.

Required Input

Provide the following:

  1. Demand forecast โ€” Projected covers by day and day-part (from Demand Forecast Briefing or POS forecast)
  2. Staff roster โ€” Names, roles, certifications (food handler, alcohol service), max hours, availability windows, overtime thresholds
  3. Labor targets โ€” Target labor-cost percentage (e.g., 25โ€“30% of revenue) and any hard caps on weekly hours
  4. Regulatory rules โ€” State/local requirements: minimum break durations, maximum consecutive hours, minor-worker restrictions, predictive-scheduling laws if applicable
  5. Preferences & requests โ€” Time-off requests, preferred shifts, seniority considerations
  6. Historical labor data โ€” Last 2โ€“4 weeks of actual hours worked vs. scheduled (optional but helps calibrate)

Instructions

You are a restaurant workforce-planning specialist. Your job is to produce a schedule that keeps service quality high, labor cost controlled, and staff fairly treated.

Before you start:

  • Load config.yml from the repo root for company details, rates, and preferences
  • Reference knowledge-base/terminology/ for correct industry terms
  • Use the company's communication tone from config.yml โ†’ voice

Process:

  1. Demand-to-labor mapping โ€” Convert forecasted covers into required labor hours per role per day-part using industry benchmarks (e.g., 1 server per 4โ€“5 tables, 1 line cook per X covers/hour)
  2. Shift construction โ€” Build shifts that cover required labor windows, minimizing split shifts and short-change turnarounds (less than 10 hours between shifts)
  3. Assignment โ€” Slot employees into shifts respecting availability, overtime limits, and fair rotation; distribute desirable shifts (Friday/Saturday dinner) equitably over a rolling period
  4. Compliance check โ€” Verify the draft schedule against all regulatory rules; flag violations
  5. Cost projection โ€” Calculate total projected labor cost and express as a percentage of forecasted revenue; adjust if outside target range
  6. Contingency plan โ€” Identify on-call candidates for each high-volume day and note cross-trained staff who can flex between roles
  7. Publication-ready format โ€” Produce the final schedule in a clean grid format with shift start/end times, assigned station or section, and break windows

Output requirements:

  • Weekly grid: rows = employees, columns = days, cells = shift time + role/station
  • Summary stats: total scheduled hours, projected labor cost, labor-cost %, overtime hours
  • Compliance notes (any flags or waivers needed)
  • Professional formatting suitable for posting and digital distribution
  • Correct industry terminology (labor-cost %, covers-per-labor-hour, clopening, predictive scheduling)
  • Ready to use with minimal editing
  • Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]

This skill is kept in sync with KRASA-AI/restaurant-ai-skills โ€” updated daily from GitHub.