Time of Day

Profile activity and productivity across the hours of the day and days of the week.

Input

The user provides: $ARGUMENTS

This may be:

• empty or "all" (default: all available sessions)
• a window like "last 30 days" or "last 90 days" to limit the analysis
• a project path to scope the analysis to one cwd

Data Sources

Endpoint	Returns
GET /api/sessions?limit=500	Sessions with started_at, ended_at, status, cwd, cost, and metadata (turn_count, total_turn_duration_ms), primary source for hour/weekday bucketing
GET /api/events?session_id=X	Events with timestamp and event_type (PreToolUse, PostToolUse, Stop, Compaction, APIError, etc.), finer-grained activity within sessions and error timing
GET /api/analytics	daily_sessions / daily_events (365d) and sessions_by_status for trend context and completion baselines

Report Sections

1. Activity by Hour of Day

Bucket sessions (by started_at) and events (by timestamp) into 24 hourly bins. Show a text bar chart of session and event counts per hour. Identify the busiest hours by raw volume.

2. Productivity by Hour of Day

For each hour bin, compute completion rate (completed / total sessions started in that hour) and average sustained turn time (total_turn_duration_ms / turn_count, ms → minutes). Distinguish "active" hours (high volume) from "productive" hours (high completion + sustained turns).

3. Day-of-Week Pattern

Bucket the same metrics into 7 weekday bins. Table: weekday, sessions, completion rate, avg cost, dominant model.

4. Peak vs. Low-Output Windows

• Peak windows: hours/days with high completion rate and long sustained turns.
• Low-output windows: hours/days with high abandonment/error/Compaction rates or fragmented short turns. Pull error timing from /api/events event types (APIError, Compaction) to corroborate.