Borrowing it
Nothing to install: this file belongs to scratchyone/voice-training-ui. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/scratchyone/voice-training-ui/main/.claude/skills/analyze-voice/SKILL.mdgit clone --depth 1 https://github.com/scratchyone/voice-training-uiWrote 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/scratchyone/voice-training-ui/analyze-voice)<a href="https://agentmods.dev/skills/scratchyone/voice-training-ui/analyze-voice"><img src="https://agentmods.dev/badge/skills/scratchyone/voice-training-ui/analyze-voice/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/scratchyone/voice-training-ui/analyze-voice"><img src="https://agentmods.dev/badge/skills/scratchyone/voice-training-ui/analyze-voice.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00087 | $0.01832 |
| Opus 5 | $0.00044 | $0.00916 |
| Sonnet 5 | $0.00017 | $0.00366 |
| Haiku 4.5 | $0.00009 | $0.00183 |
Grade A, and why
analyze-voice 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 9d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Rachel's voice (Voice Garden)
Voice Garden is a voice-feminization training tracker. Rachel records herself (usually the Rainbow Passage), tells you what she was practicing, and you analyze it. There are two layers and your job spans both:
- Standard analysis → permanent, data-driven dashboard cards + the permanent
"🎚️ Register & phrasing" visualizer. Fully automated by
analyze.py— you just run it. - Intelligent, per-recording annotations → you read the detailed data, find the single most important, specific, clockable thing to work on, and author it as custom UI: a big insight in the "🔍 Insights for this take" section, plus optional personalized woven notes.
Read CLAUDE.md for the full vibe/design. Keep everything warm, specific, and comforting
("Animal Crossing girliepop"). Numbers are a compass, not a judge. Be honest about
weaknesses, always actionable and kind.
Step 1 — Run the standard analyzer
cd /Users/rachel/Downloads/voice-training
uv run analyze.py "<path to audio>" --label "<what she was practicing>" [--note "..."] [--register-floor 130]
- Always pass
--label(her stated focus). Ask if she didn't say. - Python is managed with uv, never pip. If you edit
analyze.py, runuvx ruff check .and keep it clean. - Writes (additive, idempotent per id):
recordings.json(+ mirror indashboard-react/public/),public/analysis/<id>.json(heavy detail),public/audio/<id>. - Note the new id (printed as
entry #N) — it's the annotation filename.
Step 2 — Read the detailed data
Read dashboard-react/public/analysis/<id>.json and the new entry in recordings.json.
Shapes are in dashboard-react/src/types.ts:
Recording.register:in_register_pct,semitones_sd(raw, inflated),in_register_semitones_sd(honest melody),onset_sub_pct/mid_sub_pct/offset_sub_pct,phrases_landed_pct,n_phrases,floor_hz.RecordingDetail:frames {t[],hz[]}(hz null when unvoiced),phrases[](start/end/onset_hz/offset_hz/min_hz/started_in_register/ended_in_register/sub_register_pct).- Standard metrics:
pitch,formants,voice_quality,intensity. - If there's history, read prior entries to compare trends.
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.
- 9d ago First seen · 118 lines · 87 tokens per session scan A c2c024325606
analyze-voice is a skill published in the GitHub repository scratchyone/voice-training-ui (79 stars, last pushed 2mo ago), licensed MIT. It adds 87 tokens to every session and 1,832 once invoked, about $0.0004 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 2026-08-30.
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