wispr-analytics

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This skill should be used when analyzing Wispr Flow voice dictation history for self-reflection, work patterns, mental health insights, or productivity analytics AND when managing the Wispr Flow dictionary (adding terms, fixing mishears, exporting/importing, suggesting improvements). Triggered by requests like "/wispr-analytics", "analyze my dictations", "what did I dictate today", "wispr reflection", "add to wispr dictionary", "improve dictation", "wispr suggest", "export wispr dictionary", or any request to review voice dictation patterns or manage dictation quality.

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Sourceglebis/claude-skills
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NPX Install

npx skill4agent add glebis/claude-skills wispr-analytics

Tags

Translated version includes tags in frontmatter
wispr-flowdictation-analyticsdictionary-managementproductivity-insightsmental-health-reflection

SKILL.md Content

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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:
bash
# 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

Comparison & Graphs

  • --compare
    -- auto-compare with the equivalent previous period (week vs previous week, month vs previous month)
  • --graphs PATH
    -- generate an HTML dashboard with Chart.js graphs (implies --compare). Graphs include: daily words overlay, hourly activity, category breakdown, top apps, language distribution
bash
# Compare this month vs previous month (markdown)
python3 scripts/extract_wispr.py --period month --compare --format markdown

# Generate visual dashboard for week comparison
python3 scripts/extract_wispr.py --period week --compare --graphs /tmp/wispr-week.html

# Compare and save both markdown + graphs
python3 scripts/extract_wispr.py --period month --compare --format markdown --output report.md --graphs report.html

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:
markdown
---
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, cmuxterm, VS Code, Windsurf, Zed, Cursor, Terminal
  • ai_tools: ChatGPT, Claude Desktop, Perplexity, OpenAI Atlas, Codex
  • communication: Telegram, Messages, Slack, Zoom
  • writing: Obsidian, Notes, Chrome, Arc browser

Dictionary Management

Manage Wispr Flow's dictionary for better recognition accuracy. The dictionary JSON is version-controlled in 
~/ai_projects/claude-skills/wispr-analytics/data/dictionary.json
.

Dictionary Script

Run 
scripts/wispr_dictionary.py
for all dictionary operations:
bash
# Check database health and dictionary stats
python3 scripts/wispr_dictionary.py check

# List all entries (safe while Wispr is running)
python3 scripts/wispr_dictionary.py list
python3 scripts/wispr_dictionary.py list --filter "claude"

# Export dictionary to JSON (safe while running)
python3 scripts/wispr_dictionary.py export

# Suggest new entries by analyzing ASR vs formatted text differences
python3 scripts/wispr_dictionary.py suggest --days 30 --min-freq 3

# Add a single term (requires Wispr Flow to be QUIT)
python3 scripts/wispr_dictionary.py add "Gastown"
python3 scripts/wispr_dictionary.py add "cloud code" "Claude Code"

# Remove an entry (requires Wispr Flow to be QUIT)
python3 scripts/wispr_dictionary.py remove "old term"

# Import from JSON (requires Wispr Flow to be QUIT)
python3 scripts/wispr_dictionary.py import --dry-run
python3 scripts/wispr_dictionary.py import

Dictionary Safety Rules

CRITICAL: Wispr Flow must be quit before any write operations (add, remove, import). The script enforces this automatically. Read operations (export, list, suggest, check) are safe while Wispr is running.
Writing to the SQLite database while Wispr Flow has it open causes index corruption. Always:
  1. Check if Wispr is running: 
    pgrep -f "Wispr Flow"
  2. If running, ask user to quit first (Cmd+Q)
  3. After writes, run 
    check
    to verify integrity
  4. Restart Wispr Flow

Dictionary Entry Types

  • Recognition terms (phrase only): teaches Wispr to hear the word correctly (e.g., "Gastown", "LLM", "subagent")
  • Replacement rules (phrase → replacement): auto-corrects mishears (e.g., "cloud code" → "Claude Code", "клод дизайн" → "Claude Design")
  • Snippets (isSnippet=true): text expansion shortcuts (e.g., "my email" → "glebis@gmail.com")

Proactive Dictionary Improvement Workflow

When running analytics, also check for dictionary improvement opportunities:
  1. Run 
    suggest
    to find recurring ASR corrections
  2. Compare 
    asrText
    vs 
    formattedText
    for patterns
  3. Look for Russian/English code-switching mishears
  4. Check for new technical terms the user started using
  5. Export updated dictionary and commit to git

Notes

  • For analytics: the database is read-only; analytics never modifies Wispr data
  • For dictionary: writes require Wispr Flow to be quit first
  • 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
  • Dictionary JSON is stored at 
    ~/ai_projects/claude-skills/wispr-analytics/data/dictionary.json
    for version control