# Unjargon

> Make Claude's English or Chinese replies plain and decodable. Strips invented compressed jargon, on-the-spot metaphors, borrowed cross-domain terms, and "it's not X, it's Y" framing. Use when someone says a reply is hard to read, cryptic, pretentious, over-abstract, or full of made-up terms; when they ask you to rewrite or diagnose a confusing passage; or when they say "说人话", "看不懂", "太抽象", "别绕", "别用黑话", "plain English", "stop the metaphors", "just say it straight". Also load it before writing for a reader who is not a domain expert. This is NOT humanizer or stop-slop: those remove AI padding (empty superlatives, rule-of-three, filler). This removes AI density (prose that is too compressed and too clever). Opposite failure modes; both can run.

- Skill: `maxxiedddd/unjargon` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add maxxiedddd/unjargon`
- Raw SKILL.md: https://api.skillmd.com/api/skills/maxxiedddd/unjargon/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: maxxiedddd (https://skillmd.com/u/maxxiedddd)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/maxxiedddd/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`):

| Language | What Claude wrote | The problem |
|---|---|---|
| EN | `instrumentation is the unlock` | Aphorism invented on the spot, presented as a known saying |
| EN | `is where a VP smells hand-waving` | Metaphor the reader must decode first |
| EN | `They're tightening, term-locking, and having the counter-probe answer loaded.` | Two invented compound terms in one sentence |
| ZH | `三硬条件` | Self-coined four-character compound; looks like an established term, isn't one |
| ZH | `阳性对照失败` | Lab-statistics term dropped into a marketing context |
| ZH | `我们在一块没人占的地里排得差` | Farming metaphor for "we rank badly despite low competition" |

## 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:

```bash
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".

| Model | Need | Basis |
|---|---|---|
| Opus 4.7 | High | depth 0.23σ, caution 0.24σ — highest measured |
| Opus 4.8 | High | Subject of the 471-upvote complaint thread |
| Opus 5, Fable 5 | High | Named in bug report #77136; no official measurement published |
| Opus 4.6 | Moderate | brevity 0.08σ — "tends to get straight to the point" |
| Opus 4.5 | Low | Users repeatedly name it the last well-calibrated version |
| Sonnet 4.6 | Low | brevity 0.14σ, warmth 0.17σ — already leans short |
| Haiku | Low | Not implicated in any report found |

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

| File | Purpose |
|---|---|
| `references/rules-zh.md` | Six Chinese patterns, with fixes and examples |
| `references/rules-en.md` | Six English patterns, with fixes and examples |
| `references/evidence.md` | Every source behind these rules, with links |
| `scripts/check.js` | Mechanical checker, zero dependencies |
| `scripts/lexicon.json` | Term lists, editable |