tone-consistency-checker

tone-consistency-checker is a skill for Claude Code from ur-grue/autopunk-media-skills. It costs 35 tokens per session (1,770 once invoked), scanned A, original, MIT.

A writing checker that finds places where the voice or level of formality changes unexpectedly within a piece. It explains the shifts and suggests how to make the writing consistent.

In plain words
What is it for?
Use it on articles, newsletters, institutional reports, podcast scripts, and other writing produced by multiple people or across several sessions.
Why use it?
It helps locate uneven passages that can make a collaborative or long-running draft feel disjointed. Writers can use the report to bring the whole piece back to an intended voice.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the autopunk-media-skills plugin — 187 skills shipped together

Good fit Use it on articles, newsletters, institutional reports, podcast scripts, and other writing produced by multiple people or across several sessions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ur-grue/autopunk-media-skills/tone-consistency-checker
Install

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.

Any agent
npx skills add ur-grue/autopunk-media-skills --skill tone-consistency-checker
Clone the repo
git clone --depth 1 https://github.com/ur-grue/autopunk-media-skills

Made for: Claude Code.

Or install autopunk-media-skills, the plugin that ships this one along with the rest of its 187 skills.

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.

agentmods badge for tone-consistency-checker

README.md
[![agentmods](https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/tone-consistency-checker/github.svg)](https://agentmods.dev/skills/ur-grue/autopunk-media-skills/tone-consistency-checker)
Your own site
<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/tone-consistency-checker"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/tone-consistency-checker/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.

agentmods 80×15 button for tone-consistency-checker

Your own site · 80×15
<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/tone-consistency-checker"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/tone-consistency-checker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,770 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.01770
Opus 5 $0.00017 $0.00885
Sonnet 5 $0.00007 $0.00354
Haiku 4.5 $0.00003 $0.00177

Measured 8d ago against content hash b61e4a3e6646, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

tone-consistency-checker 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 8d 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.

skills/editing/tone-consistency-checker/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tone Consistency Checker

What This Skill Does

Identifies passages where the tone or register shifts unexpectedly within a piece of writing, and explains how to bring the text back to a consistent voice.

When To Use This Skill

  • After a draft has been written across multiple sessions and the voice may have drifted
  • When multiple writers collaborated on a single article, script, or report and their individual styles are in conflict
  • Before publishing a branded publication (newsletter, institutional report, podcast script) that must maintain a specific register throughout
  • When an editor suspects a piece feels uneven but cannot pinpoint exactly where the problem lies

What You Need To Provide

Required: The full text to be checked. Optional: A description of the intended tone (e.g., "authoritative but accessible, like a quality broadsheet feature"; "warm and conversational, like a podcast host talking to a friend"; "neutral and precise, broadcast news style"); the publication or format this is written for; any passages the writer considers the tonal benchmark — the parts they are happiest with.

How the Assistant Approaches This

  1. Reads the entire text first to establish what the dominant tone appears to be — or, if a target tone description was provided, uses that as the reference standard. Notes vocabulary level, sentence rhythm, use of contractions, formality of address, emotional temperature, and use of first or second person.
  2. Re-reads paragraph by paragraph, flagging any passage where one or more of those dimensions shifts sharply relative to the established baseline — a sudden use of jargon in a plain-language piece, an informal aside in a formal investigation, a shift from third-person reporting voice to first-person opinion.
  3. Produces a structured report: each flagged passage is quoted in full, the tonal problem is named clearly in plain language, and a rewrite suggestion is provided so the editor can either adopt it or use it to prompt their own revision.

Read the full file on GitHub · 109 lines

Files

What ships with it

1 file 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.

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.

  1. 8d ago First seen · 109 lines · 35 tokens per session scan A b61e4a3e6646

Subscribe to this mod's changes

tone-consistency-checker is a skill published in the GitHub repository ur-grue/autopunk-media-skills (32 stars, last pushed 12d ago), licensed MIT. It adds 35 tokens to every session and 1,770 once invoked, about $0.0002 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 2026-09-04.

Related

Other skills, from other repositories

cinematic-script-writer

Create professional cinematic scripts for AI video generation with character consistency and cinematography knowledge. Use when the user wants to write a cinematic script, create story contexts with characters, generate image prompts for AI video tools (Midjourney, Sora, Veo), or needs cinematography guidance (camera…

praveenspeaks/cinematic-script-writer · 94 tokens

anti-slop

Eliminate AI-sounding patterns from any written output. Applies editorial rules synthesized from the best open-source anti-slop tools: banned phrases, structural pattern detection, false agency checks, and a scoring rubric. Use as a quality gate for ANY content — blog posts, social media, emails, documentation…

TeamDay-AI/agents · 115 tokens

voice-update

Update context/voice-and-style.md or context/about-me.md from one of three sources. Manual (user dictates a single new rule, sample, or career fact). Memory (batch pull from Claude Code's auto-memory in /.claude/projects/ /memory/). Sent-mail (analyze the last 20-50 sent Gmail messages and propose updates from…

kalyvask/winning-writing · 169 tokens

yourself-story

Drafts and critiques bios, LinkedIn About sections, intro slides, "tell me about yourself" interview answers, and personal-essay openers using Adam Bryant's 500-CEO research and Lauren Weinstein's warmth+competence frame. Different from pitch-coach (which is product-shaped) and cold-email-coach (which is…

kalyvask/winning-writing · 167 tokens

compression

Cuts a draft to a target word count without losing substance. Two modes behind one skill. target-count hits a specific number (200 for a cold email, 500 for an op-ed, 6 for a product summary). redundancy catches the specific failure where a phrase says what the verb or context already implied — "going forward" after a…

kalyvask/winning-writing · 184 tokens

style-tells

Scrubs the three surface tells that make prose feel AI-generated, padded, or jargon-heavy. Three targets behind one skill, picked via the --target arg. em-dashes (default off in cold email / memo / Slack, max one per page in op-eds), adverbs (the -ly and intensifier pile), jargon (the Silicon Valley / consultant…

kalyvask/winning-writing · 173 tokens