What is analyze-voice?

A workflow for analyzing Rachel’s voice-training recordings and adding personalized guidance to a voice-feminization tracker. It combines automatic measurements with a written explanation of the main thing to practise.

Use it for new voice recordings, especially when Rachel shares what she was practising and needs analysis plus tailored notes.

It turns recording data into one specific, actionable focus while keeping feedback supportive and avoiding judgment based only on numbers.

How scratchyone/voice-training-ui configures Claude Code

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/scratchyone/voice-training-ui/main/.claude/skills/analyze-voice/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/scratchyone/voice-training-ui

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/scratchyone/voice-training-ui/analyze-voice/github.svg)](https://agentmods.dev/skills/scratchyone/voice-training-ui/analyze-voice)

Your own site

<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.

Your own site · 80×15

<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>

Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,832 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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 5.5	$0.00035	$0.00733
Sonnet 5.5	$0.00017	$0.00366
Haiku 4.5	$0.00009	$0.00183

Measured 2mo ago against content hash c2c024325606, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/skills/analyze-voice/SKILL.md · 118 lines

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, run uvx ruff check . and keep it clean.
Writes (additive, idempotent per id): recordings.json (+ mirror in dashboard-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.

Read the full file on GitHub · 118 lines

Changes

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.

2mo ago First seen · 118 lines · 87 tokens per session scan A c2c024325606

Subscribe to this mod's changes

analyze-voice is a skill published in the GitHub repository scratchyone/voice-training-ui (81 stars, last pushed 4mo ago), licensed MIT. It adds 87 tokens to every session and 1,832 once invoked, about $0.0003 per session on Opus 5.5. 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.

Analytics and metrics

analyze-voice next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
analyze-voicescratchyone/voice-training-ui	Skill	81	4mo ago	MIT	A	87	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	today	MIT	A	103	Claude Code

voice-training-ui: Skill for Claude Code