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name: time-of-day description: > Discover when you are most active and most productive with Claude Code by bucketing sessions and events into hour-of-day and day-of-week bins from their timestamps, then flagging peak versus low-output windows. Uses the session list, per-session events, and analytics daily trends. Use when planning a schedule or deciding when to do deep work versus lighter tasks.

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.

5. Schedule Recommendation

Suggest which hour/weekday blocks to reserve for deep work and which to use for lighter or shallower tasks, grounded in the buckets above.

Output

• Markdown with text-based bar charts (e.g., 09:00 ████████ 24) for the hourly and weekday distributions.
• Tables for the hour and weekday metrics; ▲ / ▼ for above/below the overall mean.
• Currency in USD to 4 decimals; durations in minutes (convert from ms).
• Cite only numbers from the API. State how many sessions/events were bucketed and exclude sessions missing started_at or the focus metadata, noting the count.