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-writergit 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-writer)<a href="https://agentmods.dev/skills/kkarpushin/tone-of-voice/tone-of-voice-writer"><img src="https://agentmods.dev/badge/skills/kkarpushin/tone-of-voice/tone-of-voice-writer/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-writer"><img src="https://agentmods.dev/badge/skills/kkarpushin/tone-of-voice/tone-of-voice-writer.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 | $0.00128 | $0.01080 |
| Opus 5 | $0.00064 | $0.00540 |
| Sonnet 5 | $0.00026 | $0.00216 |
| Haiku 4.5 | $0.00013 | $0.00108 |
Grade A, and why
tone-of-voice-writer 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tone of Voice Writer
You write texts in a voice previously captured by tone-of-voice-analyzer. Your job is not to write well in general — it is to write as this specific author would, including their flaws, quirks, and limits.
STEP 1 — Locate the profile
- If the user gave a path — use it.
- Otherwise search:
Glob **/tone-of-voice/*/profile.md. Several found → ask which (AskUserQuestion). None found → say a profile is needed first and offer to run tone-of-voice-analyzer.
STEP 2 — Clarify the brief
Before drafting, know all of:
- Format & platform (LinkedIn post, email, article, Telegram reply…) — platform norms interact with voice.
- Audience & context — map to a row of the profile's §8 context matrix (celebrating / problem / teaching / selling / …). If the context isn't in the matrix, pick the nearest row and say so.
- Topic, key message, target length.
- Target language. If the profile has no lexical code for that language (§4), warn: the voice is unprofiled in this language; extrapolate register and syntax but invent no pet words; flag the result as low-confidence.
Ask only for what's missing — one AskUserQuestion round, not an interview.
STEP 3 — Load the voice
From profile.md take, in priority order:
- §11 Machine block — the condensed identity; treat as your system instruction.
- §4 Lexical code for the target language — signature words to draw from (naturally, not as a checklist), and the never use list as hard bans.
- §8 Context matrix — the row matching the brief.
- §9 Do/Don't and §5 Syntactic DNA (sentence-length numbers are targets, not decoration).
STEP 4 — Draft
Write the text. Signature devices and pet words appear where the author would use them — a voice is a distribution, not a stamp; do not cram every marker into every paragraph.
STEP 5 — Verify before delivering (mandatory)
- Ban check (mechanical). Grep the draft for every item in the profile's never use list. Any hit → rewrite that spot.
- Metric check. If the analyzer's script is available, run it on the draft:
Compare sentence-length mean/mix, punctuation, and emoji/CAPS rates against the profile's §5/§7 numbers. Outside the author's typical range → adjust. (Short drafts are noisy — compare patterns, not decimals.)python3 <analyzer-skill-dir>/scripts/metrics.py draft.md - Do/Don't pass. Reread the draft against each §9 pair.
- Overshoot check. Verify against every attribute's "But not:" — imitation most often fails by exaggeration (a "direct" voice becomes rude, a "warm" one becomes saccharine).
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 · 80 lines · 128 tokens per session scan A 50e1698b9d1f
tone-of-voice-writer is a skill published in the GitHub repository kkarpushin/tone-of-voice (5 stars, last pushed 18d ago), licensed MIT. It adds 128 tokens to every session and 1,080 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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