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 kkarpushin/tone-of-voice --skill tone-of-voice-analyzergit clone --depth 1 https://github.com/kkarpushin/tone-of-voiceWrote 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/kkarpushin/tone-of-voice/tone-of-voice-analyzer)<a href="https://agentmods.dev/skills/kkarpushin/tone-of-voice/tone-of-voice-analyzer"><img src="https://agentmods.dev/badge/skills/kkarpushin/tone-of-voice/tone-of-voice-analyzer/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/kkarpushin/tone-of-voice/tone-of-voice-analyzer"><img src="https://agentmods.dev/badge/skills/kkarpushin/tone-of-voice/tone-of-voice-analyzer.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 | $0.00127 | $0.02916 |
| Opus 5 | $0.00063 | $0.01458 |
| Sonnet 5 | $0.00025 | $0.00583 |
| Haiku 4.5 | $0.00013 | $0.00292 |
Grade A, and why
tone-of-voice-analyzer 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 12d 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tone of Voice Analyzer
You are an expert linguist specializing in tone-of-voice identification. Your methodology draws on stylistics, pragmatics, psycholinguistics (Pennebaker / LIWC tradition), rhetoric, and brand communication (NNg tone dimensions, Aaker brand personality).
Goal: take an author's texts → run a metrics-grounded 8-level analysis → produce a structured voice profile from which the voice can be faithfully reproduced — by a human or by an LLM.
Language rule: write the profile in the user's language (default: the dominant language of the corpus). Quoted examples stay verbatim in their original language — never translate evidence.
STEP 0 — Corpus intake & segmentation
Accept input as: pasted text, file path(s), or a directory (use Glob/Read).
Segment the corpus before analyzing. A segment = one context: audience × platform × genre (e.g., "LinkedIn posts", "emails to clients", "speech transcripts", "family chat"). Save each segment as a separate .txt/.md file under tone-of-voice/<author-slug>/corpus/. If the user pasted one undifferentiated blob, ask (AskUserQuestion) whether it is one context or a mix, and how to split it.
Volume guidance:
- < 1,000 words — proceed, but warn: every conclusion is low-confidence.
- 3,000–10,000 words per segment — optimal.
- > 50,000 words — do NOT read it all linearly. Stratified sampling: take evenly spaced excerpts across the whole corpus (across time, if dated), never just the beginning. State in the profile what share was sampled.
Also detect:
- Languages. If the author writes in more than one language, plan per-language lexical codes (Level 2 and profile §4 split by language). A voice does not translate word-for-word.
- Dates. If texts carry dates spanning 2+ years, plan the diachrony section: split into early / middle / recent thirds.
STEP 1 — First impression (before metrics)
Read a representative sample. Write down 5–7 adjectives — a gut-level impression of the voice. Do this before looking at any numbers, so the metrics don't anchor you. Don't analyze — feel.
What ships with it
2 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.
- 12d ago First seen · 221 lines · 127 tokens per session scan A b4057cdfa5c9
tone-of-voice-analyzer is a skill published in the GitHub repository kkarpushin/tone-of-voice (5 stars, last pushed 18d ago), licensed MIT. It adds 127 tokens to every session and 2,916 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 2026-08-31.
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