wispr-analytics（维斯普分析）

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.sqlite

Key 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.md

Period Options

• today -- current day (default)
• yesterday -- previous day
• week -- last 7 days
• month -- last 30 days
• YYYY-MM-DD -- specific date
• YYYY-MM-DD:YYYY-MM-DD -- date range

Mode Options

• all -- full analysis (default)
• technical -- filters to coding/AI tool dictations
• soft -- filters to communication/writing dictations
• trends -- focus on volume/frequency patterns
• mental -- 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 asrText field contains raw speech recognition before formatting -- useful for detecting speech patterns vs formatted output