Seasonal Crew Availability Map

By merging calendars, time logs, route/job spreadsheets, and manager notes into a single seasonal roster and capacity model (steps 2–5 and 9), users gain a clear, period-by-period view of who is available, where crews are assigned, and which jobs are planned. This consolidated view accelerates learning about how staffing patterns, PTO, and recurring jobs interact over a season so planners can quickly spot coverage clusters, recurring shortfalls, and peak demand windows.

Using explicit policy capture (step 1) and computed metrics (steps 4–6 and 10), the system flags overtime risk and jurisdiction/union rule violations before they occur. This helps users learn the thresholds and causes of overtime (e.g., recurring jobs, travel buffers, or single-person dependencies) and empowers them to choose compliant mitigation strategies rather than reacting to payroll surprises.

When gaps or skill mismatches are detected (step 6), the workflow generates 1–3 ranked coverage options per job (step 7) — e.g., intra-crew re-sequencing, cross-crew borrowing, partial splits, deferral, or approved overtime — including trade-offs. This teaches users practical decision paths by showing feasible alternatives, the impact of each choice, and why one option is preferred, enabling faster, more defensible operational decisions.

By producing standardized artifacts (crews.csv, jobs-needing-backup.csv, schedule-changes-for-approval.csv, availability-map.md, and optional workbook) and surfacing validation checks and assumptions (steps 9–11), the skill creates materials ready for review, approval, and learning. Users can trace flagged issues back to source data, review assumptions, iterate when inputs change, and build institutional knowledge about scheduling impacts across seasons.