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 alebgl77/claude-inc --skill voice-buildergit clone --depth 1 https://github.com/alebgl77/claude-incWrote 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/alebgl77/claude-inc/voice-builder)<a href="https://agentmods.dev/skills/alebgl77/claude-inc/voice-builder"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/voice-builder/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/alebgl77/claude-inc/voice-builder"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/voice-builder.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 | $0.00110 | $0.01076 |
| Opus 5.5 | $0.00044 | $0.00430 |
| Sonnet 5.5 | $0.00022 | $0.00215 |
| Haiku 4.5 | $0.00011 | $0.00108 |
Grade A, and why
voice-builder 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Builder — Voice Coach
"Clone your voice"
When to use
- "Everything you write sounds like AI — make it sound like me."
- Before any recurring content work: build the profile once, reuse it everywhere.
- "Clone my voice from these posts." / "Here are my last 15 newsletters."
- Ghostwriting for a founder whose voice must survive the ghostwriter.
- Recalibrating after feedback: "that draft didn't sound like me."
Workflow
- Collect 5–20 samples of the user's own unedited writing — posts, emails, DMs, talk transcripts. Reject copy written by others or by AI. Under five usable samples: proceed, but stamp the profile low-confidence and name what's missing.
- Run the mechanical analysis: sentence-length distribution (short / medium / long %), paragraph size, punctuation quirks (em-dashes, ellipses, parentheticals, one-word sentences), capitalisation habits, emoji policy.
- Extract the lexical fingerprint: signature phrases and crutch words, a DO list (words they reach for), a DON'T list (words they would never use), slang and profanity tolerance.
- Map the habits: how they open (question, scene, blunt claim), how they close (CTA, punchline, trail-off), how they handle transitions and humour.
- Set the tone sliders — formal↔casual, warm↔dry, confident↔hedged, dense↔airy — each a 1–10 position justified by a quoted sample line.
- Write three calibration paragraphs on neutral topics in the reconstructed voice, each with a self-check: which rules it exercised, where it drifted.
- Split the rules into HARD (never break) and SOFT (default, bendable), then
save the file as
voice-profile.mdat the project root. - Hand off: announce that the profile exists and that post-writer, reels-scripting, and profile-optimizer must load it before writing anything.
Output format
The deliverable is a file, voice-profile.md:
# Voice Profile: <name>
Confidence: high | medium | low (<n> samples: <sources>)
## Sentence mechanics
- Length mix: ~<x>% short (<8 words), <y>% medium, <z>% long
- Paragraphs: <typical size and rhythm>
- Punctuation quirks: <em-dashes, one-word sentences, ellipses...>
## Lexicon
- Signature phrases: "<...>", "<...>", "<...>"
- DO: <words and constructions> | DON'T: <words and constructions>
## Habits
- Openers: <pattern + quoted example> | Closers: <pattern + quoted example>
- Transitions and humour: <how>
## Tone sliders (1–10, each with evidence)
- formal <n> casual — "<quoted line>"
- warm <n> dry — "<quoted line>"
- confident <n> hedged — "<quoted line>"
- dense <n> airy — "<quoted line>"
## Hard rules (never break)
1. <rule>
## Soft rules (defaults, bendable)
1. <rule>
## Calibration
### Test 1: <neutral topic>
<paragraph written in the voice>
Self-check: <rules exercised; drift named>
### Test 2 / Test 3
<same structure>
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.
- 1mo ago First seen · 100 lines · 110 tokens per session scan A 09aa34552481
voice-builder is a skill published in the GitHub repository alebgl77/claude-inc (19 stars, last pushed 23d ago), licensed MIT. It adds 110 tokens to every session and 1,076 once invoked, about $0.0004 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 2026-09-04.
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