Sprint planning tool now flags unrealistic timelines by analyzing calendar availability

Many project management platforms—including Jira, Linear, Asana, and Notion—assume tasks can be completed in isolation, with no regard for actual scheduling constraints. To address this gap, a lightweight automation was developed that integrates Google Calendar and Calendly data to preemptively question overambitious sprint commitments before they’re finalized.

Each morning at 7 AM, a Cloud Function pulls all scheduled events for the next two weeks, then calculates uninterrupted focus windows—defined as any continuous block of at least 90 minutes without meetings or recurring commitments. These windows are cross-referenced against the current sprint backlog. If a task’s estimated effort exceeds the available focus time before its deadline, the system automatically posts a Slack notification suggesting adjustments: either splitting the task, delaying its due date, or revising the scope.

def compute_focus_windows(calendar_events, min_block_minutes=90):
    """Return list of (start, end) tuples where deep work can happen."""
    busy = sorted([(e.start, e.end) for e in calendar_events])
    windows = []
    day_start = datetime.combine(date.today(), time(9, 0))
    day_end   = datetime.combine(date.today(), time(18, 0))

    cursor = day_start
    for b_start, b_end in busy:
        if b_start - cursor >= timedelta(minutes=min_block_minutes):
            windows.append((cursor, b_start))
        cursor = max(cursor, b_end)
    if day_end - cursor >= timedelta(minutes=min_block_minutes):
        windows.append((cursor, day_end))
    return windows

Since its implementation, the team has eliminated inflated estimates, as the copilot exposed hidden buffers in scheduling. To further reduce interruptions, a "protect focus" feature was added, which automatically declines meeting requests during designated deep-work blocks with a courteous auto-reply. This change alone cut context-switching by about 40% within the first 14 days.

The current version does not factor in individual energy fluctuations, treating all focus windows equally regardless of time of day. The next update will introduce productivity multipliers, adjusting window values based on historical commit patterns and personal peak hours.

The entire solution is built with approximately 200 lines of Python and relies on Firestore for caching.