Unjargon

Unjargon

Claude's recent models compress prose until it stops being readable. This skill undoes that.

The failure is not length. It is density: a sentence that carries three ideas in nine words, using a term the reader has never seen, wrapped in a metaphor invented two seconds ago. The reader has to decode it before they can disagree with it.

What this fixes

Real examples, collected from public bug reports (see references/evidence.md):

What this does NOT fix

• Padding, filler, empty praise, rule-of-three. That is the opposite problem. Use humanizer or stop-slop.
• Response length. A reply can be long and perfectly clear. If the complaint is "too long", use Claude Code's built-in Concise output style instead.
• Code, comments, commit messages, API docs, technical specs. Industry terms belong there. Rewriting idempotent as "safe to run twice" inside a code comment makes it worse. This skill covers prose addressed to a human reader.

The one rule that matters most

Never ask for "concise". Ask for specific banned constructions.

Models reliably read "be concise" as "be denser", which is exactly the disease. This is documented user experience, not theory:

"Models are very good at interpreting 'concise' as 'dense and weird.'" — r/ClaudeAI, 2026-07

Every rule below names a construction to avoid, not a quality to aspire to.

How to use this skill

Mode 1 — Diagnose

The user pastes something they could not parse. Do not rewrite yet.

1. Quote the exact span that is hard to read.
2. Name which of the six patterns it is (see the rules file for your language).
3. Say what it was probably trying to mean.
4. Ask if they want it rewritten, or if they want the underlying claim checked.

Diagnosis comes first because a hard-to-read sentence is sometimes hiding a claim with no evidence behind it. Rewriting it into plain language can make a weak claim sound reasonable.

Mode 2 — Rewrite

Load the rules file for the target language and apply it:

• Chinese → references/rules-zh.md
• English → references/rules-en.md

Then verify mechanically:

node scripts/check.js draft.md

The script flags known jargon, invented compounds, "not X but Y" framing, and metaphor markers. It exits 1 when it finds something, so it works in CI. Read scripts/README.md for flags.

A rewrite is done when: every term is either common usage or defined on first appearance; the point survives if you delete every metaphor; and the first sentence states the conclusion.

Mode 3 — Install permanently

Rules that live in conversation context drift. Users report the tone reverting after a handful of turns. The durable fix is the system prompt.

Ask the user which environment they are in, then:

Claude Code — copy output-styles/unjargon.md to ~/.claude/output-styles/, then run /config → Output style → Unjargon. Takes effect after /clear or a new session.

Note: /output-style was removed in Claude Code v2.1.91. Use /config.

Claude.ai web or desktop — paste standalone/claude-ai-style.md into Settings → Custom styles → Create style → describe the style.

API — prepend standalone/system-prompt-en.md or -zh.md to your system prompt.

Third-party tools running Claude (Cursor, Cline, Windsurf) — see standalone/third-party-tools.md. These rules are tuned to Claude's specific habits. If the underlying model is GPT or Gemini, the dominant complaint is padding rather than density, and these rules may not help.

Which models need this

Based on Anthropic's own published measurements plus public bug reports. The σ values are from Anthropic's study of 309,815 conversations, on a depth-versus-brevity axis defined as "whether Claude leans toward explaining in depth or doing only what was asked".

Two honest caveats:

1. Anthropic measured only Sonnet 4.6, Opus 4.6, and Opus 4.7. Ratings for Opus 4.8, Opus 5, and Fable 5 rest on user reports, not published numbers.
2. This is not "smarter models are worse". Opus 4.5 was equally capable and is widely called the most readable. The pattern appears to come from a training change after 4.5, not from capability itself.

Language coverage

English and Chinese only.

English is where the effect is strongest. Anthropic's data puts English at the top of the depth axis, and the largest public complaint threads are from native English speakers. Chinese is included because it adds a second problem on top: Chinese has no spaces between words, so four characters run together read like an established technical term even when the model just invented them. 三硬条件 looks like something you should already know. three hard filters cannot hide the same way.

Other languages are out of scope. Anthropic's study covers 20 languages and found real per-language differences, but reported no specific findings for the languages this skill would need rules for.

Files