Preface

When reviewing the tasks completed with WorkBuddy during a specific period—such as the time spent on each task, credits consumed, or the number of conversation turns—most people rely on scrolling through chat logs to estimate. The problem with estimation is that tasks may span across days or windows, mixing new sessions with continued ones, and it is difficult to distinguish between machine runtime and actual user participation time. This Skill extracts local usage data, categorizes statistics by task, and outputs sortable Markdown tables.

What is this

WorkBuddy Usage Stats (Coordinate @user_8149be50/workbuddy-usage-stats) is a community-sourced Skill on SkillHub, published and maintained by user_8149be50. The current version is 1.2.1, categorized under data-analysis, with the tag requires_api_key=false (no additional API Key required).

It solves the problem of breaking down local WorkBuddy usage into machine hours and user hours, then statistically analyzing active duration, user duration, credit consumption, and conversation turns by task dimension. This is used for personal usage reviews and cost assessments.

Core Features

  1. Distinguishes between new tasks and continued tasks. The judgment is based on whether created_at falls within the statistical window: those within the window are counted as new tasks, while those spanning across windows are counted as continued tasks.
  2. For continued tasks, it only counts the turns and duration within the current window to avoid double-counting usage from the previous window.
  3. Statistics cover four dimensions for each task: actual active duration, user duration, Credits consumed, and conversation turns.
  4. Outputs sortable Markdown tables, facilitating review by sorting based on duration or credits.
  5. Supports customizing the interval threshold for user duration to define the criteria for “user participation time.”

Among these, “Active Duration” and “User Duration” represent two different metrics: the former leans towards actual machine operation, while the latter leans towards actual human investment. Listing them separately facilitates cost and effort assessment.

Installation and Activation

Official documentation does not provide git clone, command-line installation, or configuration file paths, nor does it offer separate instructions for Cursor, Codex CLI, or Claude Code. At this stage, only localization information is provided:

SkillHub Page: https://www.skillhub.cn/skills/user_8149be50/workbuddy-usage-stats
Coordinate: @user_8149be50/workbuddy-usage-stats
Version: 1.2.1
Source: community

This type of Skill is based on the general SKILL.md format and can usually be triggered in a conversation once placed in the corresponding Agent’s Skill directory. Specific directory paths and activation methods should be based on the SkillHub page or your Agent’s documentation; this page does not provide them, and no speculation is made here.

Typical Usage

Statistical trigger words cover the following types of requests; simply state your needs:

Show this week's task stats
How many credits did I use this week
Show my task list from last week
How long was I actively working in tasks this time
Show today's usage stats

The usage method involves providing a time window (this week, last week, today), and the Skill calculates the task list, duration, and credits based on that window, outputting a Markdown table.

Applicable Scenarios & Notes

Suitable for people who need to regularly review WorkBuddy usage: those who want to see how much time was spent on each task, how many credits were consumed, how many conversation turns occurred, or want to estimate the actual time they invested in tasks.

Please note three points before using:

  1. The scope of statistics is local usage data, not the entire cloud history; cross-machine usage is not included in this metric.
  2. New and continued tasks are divided by created_at and the time window. Continued tasks only count usage within the window; to get the complete usage, you need to perform statistics window by window.
  3. User duration depends on the interval threshold; different thresholds yield different results. It is recommended to clarify this metric before reviewing.

Conclusion

The value of this Skill lies in turning “roughly how much was used” into specific, sortable, and verifiable numbers, separating machine hours from user hours, and separating new tasks from continued ones. Official URL: https://www.skillhub.cn/skills/user_8149be50/workbuddy-usage-stats (Data API: https://api.skillhub.cn/user_8149be50/workbuddy-usage-stats).