Wispr Analytics
Extract and analyze Wispr Flow dictation history from the local SQLite database. Combine quantitative metrics with LLM-powered qualitative analysis for self-reflection, work pattern recognition, and mental health awareness.
Data Source
Wispr Flow stores all dictations in SQLite at:
~/Library/Application Support/Wispr Flow/flow.sqliteKey table: History with fields: formattedText, timestamp, app, numWords, duration, speechDuration, detectedLanguage, isArchived.
The user has ~8,500+ dictations since Feb 2025, bilingual (Russian/English), across apps: iTerm2, ChatGPT, Arc browser, Claude Desktop, Windsurf, Telegram, Obsidian, Perplexity.
Extraction Script
Run scripts/extract_wispr.py to pull data from the database:
# Get today's data as JSON with stats + text samples
python3 scripts/extract_wispr.py --period today --mode all --format json
# Get markdown stats for the last week
python3 scripts/extract_wispr.py --period week --format markdown
# Get text samples only for LLM analysis
python3 scripts/extract_wispr.py --period month --mode mental --texts-only
# Save to file
python3 scripts/extract_wispr.py --period week --format markdown --output /path/to/output.mdPeriod Options
today-- current day (default)yesterday-- previous dayweek-- last 7 daysmonth-- last 30 daysYYYY-MM-DD-- specific dateYYYY-MM-DD:YYYY-MM-DD-- date range
Mode Options
all-- full analysis (default)technical-- filters to coding/AI tool dictationssoft-- filters to communication/writing dictationstrends-- focus on volume/frequency patternsmental-- all text, framed for wellbeing reflection
Workflow
Step 1: Extract Data
Run the extraction script with the requested period and mode. Use --format json for full data or --texts-only for LLM analysis focus.
Step 2: Present Quantitative Stats
Display the quantitative summary first:
- Total dictations, words, speech time
- Category breakdown (coding, ai_tools, communication, writing, other)
- Language distribution
- Hourly activity pattern
- Daily trends (for multi-day periods)
- Top apps
Step 3: Perform Qualitative Analysis
Read references/analysis-prompts.md to load the appropriate analysis template for the requested mode. Then analyze the text samples using that template.
For each mode:
Technical: Focus on what was worked on, technical decisions, context-switching patterns, productivity assessment.
Soft: Focus on communication style shifts, language-switching patterns, audience adaptation, interpersonal dynamics.
Trends: Focus on volume changes, time-of-day shifts, app migration, behavioral change hypotheses.
Mental: Focus on energy proxies, sentiment signals, rumination detection, activity pattern changes. Frame all observations as invitations for self-reflection, never as diagnoses. Use language like "you might notice..." or "this pattern could suggest..."
All: Combine all four perspectives into a unified reflection.
Step 4: Output
Default output location: meta/wispr-analytics/YYYYMMDD-period-mode.md in the vault.
File format:
---
created_date: '[[YYYYMMDD]]'
type: wispr-analytics
period: [period description]
mode: [mode]
---
# Wispr Flow Analytics: [period]
## Quantitative Summary
[stats from Step 2]
## Analysis
[qualitative analysis from Step 3]
## Reflection Prompts
[3-5 questions based on observations]If the user requests console-only output, skip file creation and display directly.
App Category Mapping
The extraction script categorizes apps:
- coding: iTerm2, VS Code, Windsurf, Zed, Cursor, Terminal
- ai_tools: ChatGPT, Claude Desktop, Perplexity, OpenAI Atlas
- communication: Telegram, Messages, Slack, Zoom
- writing: Obsidian, Notes, Chrome, Arc browser
Notes
- The database is read-only; this skill never modifies Wispr data
- Text samples are capped at 100 per extraction to manage context window
- For multi-day periods, daily trend tables help visualize changes
- Bilingual dictations are common; analysis should honor both Russian and English
- The
asrTextfield contains raw speech recognition before formatting -- useful for detecting speech patterns vs formatted output