Analyze Wispr Flow dictation history and generate structured reports.
npx skills add https://github.com/KISHOR403/claude-skills --skill wispr-analytics
Please help me install this Agent Skill. Skill: wispr-analytics Source: https://github.com/KISHOR403/claude-skills/tree/main/wispr-analytics Command: npx skills add https://github.com/KISHOR403/claude-skills --skill wispr-analytics
Wispr Flow dictation history contains valuable patterns about work habits and wellbeing that are difficult to surface manually. This skill provides an automated pathway to extract quantitative metrics and generate qualitative insights for self-reflection.
Run the Wispr analytics extraction to generate a report from your local Wispr Flow data.
High-intent search queries and answers about installing and using this skill.
Analyzing Wispr Flow dictation history for self-reflection involves extracting dictation counts, word counts, and language distributions from the local SQLite database to surface work habit patterns and wellbeing insights.
You can identify patterns in dictation volume, language usage, and app distribution from voice dictation data to generate both quantitative metrics and qualitative wellbeing insights.
Extracting quantitative metrics from a local SQLite dictation database involves querying the history table to retrieve dictation counts, durations, and language distributions for multi-day periods.
Yes, generating mental wellbeing and trend reports from voice dictation logs is possible by applying specialized analytical modes to tailor qualitative reflections and quantitative metrics across multi-day periods.
The best way to export curated dictation text samples for LLM analysis is to generate a structured report in Markdown or JSON format containing period context, mode, stats, trends, and the samples.
You need access to the local Wispr Flow SQLite database containing the history table to successfully analyze dictation volume, language usage, and app distribution metrics.
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