Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add glebis/claude-skills --skill wispr-analyticsgit clone --depth 1 https://github.com/glebis/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/glebis/claude-skills/wispr-analytics)<a href="https://agentmods.dev/skills/glebis/claude-skills/wispr-analytics"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/wispr-analytics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/glebis/claude-skills/wispr-analytics"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/wispr-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00127 | $0.03599 |
| Opus 5 | $0.00063 | $0.01800 |
| Sonnet 5 | $0.00025 | $0.00720 |
| Haiku 4.5 | $0.00013 | $0.00360 |
Grade A, and why
wispr-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 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 reflectionprosody-- audio-based: pitch/intensity/voice-quality from recorded WAV (separate scriptscripts/extract_prosody.py; recent dictations only). See "Prosody Mode" below.
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
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 316 lines · 127 tokens per session scan A 82ab513cc9e7
wispr-analytics is a skill published in the GitHub repository glebis/claude-skills (374 stars, last pushed 7d ago), licensed MIT. It adds 127 tokens to every session and 3,599 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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