Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Added a clear privacy and security notice outlining risks of exposing sensitive window titles from ActivityWatch logs. - Updated requirements to specify Node.js as a prerequisite. - Enhanced the skill description with privacy recommendations and explicit user consent. - Command output now warns if raw window titles are included, encouraging aggregation for better privacy. - Added best practices section advising users on redaction, minimal data use, and local execution.
What This Skill Does
Connects to a locally running ActivityWatch instance to fetch computer activity data and run an AI-powered productivity analysis. The agent summarizes time spent per application, flags distracting patterns, and gives specific behavioral suggestions. Window titles are included by default, so data containing sensitive content is sent to the model for processing.
ActivityWatch stores data locally on 127.0.0.1, so no third-party time-tracking account or cloud sync is required before analysis.
When to use it
- Reviewing how much coding time vs meeting time was logged today
- Spotting excessive YouTube or Reddit usage during work hours
- Understanding app-switching frequency on high-distraction days
- Getting a structured daily screen-time breakdown without a paid tracker
- Auditing whether deep-work blocks actually happened as planned
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Give me a summary of my computer activity for the last 24 hours
- 1Runs `node scripts/fetch_activity.js --hours 24` against the local ActivityWatch API
- 2Checks whether output contains raw window titles and warns the user if sensitive content is detected
- 3Groups activity by application and calculates time spent in each
- 4Identifies anomalies such as frequent context switches or extended use of non-work sites
- 5Produces a time-distribution summary and 2-3 specific productivity suggestions
A breakdown of time per app category, flagged distractions (e.g., 3h 20m on YouTube), and concrete advice such as scheduling focused blocks using the Pomodoro technique
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.