Correlate Git commit history and AI conversation logs to estimate time spent.
npx skills add https://github.com/toqoz/config --skill retro-activity
Please help me install this Agent Skill. Skill: retro-activity Source: https://github.com/toqoz/config/tree/main/home/agents/skills/retro-activity Command: npx skills add https://github.com/toqoz/config --skill retro-activity
This Skill addresses the lack of visibility into personal work patterns, helping you identify time-sinks, context-switching costs, and opportunities for better AI leverage.
Run the retro-activity skill to generate a retrospective report of your work patterns for the current week.
High-intent search queries and answers about installing and using this skill.
You can analyze git commit history to estimate time spent by correlating commit timestamps with AI conversation logs to identify productivity patterns. This process visualizes time distribution across local repositories and calculates work baselines.
Yes, you can generate a weekly retrospective from local repositories managed by ghq. The skill aggregates local project metadata, git commits, and AI conversation logs to produce a retrospective report detailing your work rhythm.
The best way to identify context-switching costs is by analyzing developer work activity through correlated git commits and AI conversation logs. This highlights time-sinks and visualizes how time is distributed across different repositories.
Yes, this work pattern analysis requires Python 3 to run. It uses Python scripts to process local project metadata, git commit history, and AI conversation logs to generate actionable improvement suggestions.
AI conversation logs improve work pattern analysis by providing data to benchmark actual work time against a calculated normal baseline. This correlation highlights where AI provides the most value and identifies time-sinks.
A developer time-tracking retrospective provides concrete, data-backed suggestions to improve your work rhythm, prioritization, and commit habits for the following week based on aggregated git history and AI conversation efficiency.
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