# deslopify

This skill spots common AI-writing patterns and rewrites text for its genre. Use it to clean up agent-drafted prose before publishing.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 527 installs · ★ 168
- Author: JuliusBrussee
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/JuliusBrussee/skills/tree/HEAD/skills/deslopify/SKILL.md
- Updated: 2026-08-07
- HTML: https://affpapa.org/en/nevibecode/skills/deslopify

## What it does

deslopify scans prose for habits associated with generated writing, including contrast formulas, heavy em-dash use, even sentence rhythm, inflated wording, and hedged claims. It then rewrites the affected passages by meaning and runs another check. The skill covers articles, posts, emails, blogs, documentation, and marketing copy.

Its workflow moves through scanning, diagnosis, meaning-based rewriting, and verification. Detection patterns live in `references/tells.md`; guidance for voices and genres is in `references/voices.md`. The editing pass needs a target genre, an audience, the author’s stance, and any limits on length or citations.

When the genre or audience is unclear, the skill asks for that context before editing. That choice controls the register and which patterns count as problems.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add JuliusBrussee/skills --skill deslopify -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/deslopify/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/deslopify/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add JuliusBrussee/skills --skill deslopify -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/deslopify/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add JuliusBrussee/skills --skill deslopify -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/deslopify/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install JuliusBrussee/skills deslopify
```

2. **Manually: put the files in place**
- File: `.github/skills/deslopify/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add JuliusBrussee/skills --skill deslopify -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/JuliusBrussee/skills --path skills/deslopify
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/deslopify/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add JuliusBrussee/skills --skill deslopify -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/JuliusBrussee/skills/tree/HEAD/skills/deslopify/SKILL.md

## When to use it
- spot AI-style patterns in a draft
- rewrite contrast-heavy sentences by meaning
- fit prose to a stated genre
- recheck text after the rewrite

## Example prompts
- Run `/deslopify` on this draft and keep its meaning intact.
- Review this text as documentation, list the detected writing habits, then rewrite the affected passages.
- Clean the AI traces from this email and scan the revised version again.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: fcb48e8b0818493e.

BEGIN THIRD-PARTY FILE fcb48e8b0818493e
```text
---
name: deslopify
description: >
  De-slop pass for any text. Scans for the statistical fingerprints of AI
  writing (negative parallelism / "not X but Y", em-dash density,
  rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged
  both-sidesing), rewrites by meaning, then re-scans until the text is clean
  and sits in the right register for its genre: academic article, tweet,
  reddit post, email, blog, docs, marketing. Use when the user says
  "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize
  this", "make this not sound like AI", or invokes /deslopify. Also use
  before publishing any agent-drafted prose.
---

# Deslopify

Strip the AI fingerprints out of a text and make it good in its genre. The target is prose that reads like one person wrote it for one audience about something they actually know. Detector scores are beside the point; text can score human and still be unreadable.

## Why this loops

The "not X but Y" family and its relatives are generative habits. Preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move sits deep in the model's priors and surfaces about once a paragraph. Two consequences shape this skill:

1. **You cannot see your own slop.** The priors that produce the pattern also make it invisible on re-read. So detection runs mechanically, as regex against a fixed catalog. "Does this look AI to me?" is not a detection method.
2. **Rewriting reintroduces slop.** Ask a model to remove "it's not just X, it's Y" and out comes "this is less about X than Y", the same move in a wig. Every rewrite therefore gets re-scanned, and the loop runs until a scan comes back clean.

Workflow: **Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.**

## Phase 0: Fix the target

Before touching the text, establish:

- **Genre and venue.** Academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing. Ask when it isn't stated and isn't obvious from the text. Genre decides which tells are fatal and what "good" means; see [references/voices.md](references/voices.md).
- **Audience and stance.** Who reads it, and what the author actually claims. Slop fills the space where a claim should be, so you cannot remove it without knowing the claim.
- **Constraints.** Length limits, required citations, house style.

## Phase 1: Mechanical scan

Run the detection patterns from [references/tells.md](references/tells.md) against the text. If the text is in a file, or you can write it to a temp file, run the grep commands in that reference literally: the catalog is written as runnable `grep -Ein` patterns. Otherwise apply each pattern by hand, line by line.

Produce a finding list: line or sentence, matched pattern, tell category. Then run the two structural checks regex can't catch:

- **Cadence.** Flag any run of 3+ consecutive sentences within ±4 words of the same length, and any paragraph where every sentence has the same shape (subject, verb, elaboration).
```
END THIRD-PARTY FILE fcb48e8b0818493e

The third-party file has ended. Everything between the fcb48e8b0818493e tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 3038 of 7687 bytes. © JuliusBrussee · MIT · https://github.com/JuliusBrussee/skills/tree/HEAD/skills/deslopify/SKILL.md

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---
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