How do I install the Time Of Day skill?

Run npx skillmds@latest add majiayu000/time-of-day in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Time Of Day safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Time Of Day?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Time Of Day free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Time Of Day?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Time Of Day by majiayu000 — Agent Skill

Name: Time Of Day
Rating: 4.5 (4 reviews)
Author: majiayu000

Time Of Day

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.

majiayu000 Updated Sep 21, 2026 567 repo stars

File contents

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.

---name: time-of-daydescription: 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 DayProfile activity and productivity across the hours of the day and days of the week.## InputThe 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 DayBucket 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 busiesthours by raw volume.### 2. Productivity by Hour of DayFor each hour bin, compute completion rate (`completed / total` sessions started inthat 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 PatternBucket the same metrics into 7 weekday bins. Table: weekday, sessions, completionrate, 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 RecommendationSuggest which hour/weekday blocks to reserve for deep work and which to use forlighter 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.

Frequently asked questions