# Slopmonster > Turn AI-written copy into copy a human would ship. Lint for AI tells, rewrite, cleanse with a rival model, lint again. Trigger on /slopmonster, "humanize this", "de-slop this", "does this sound like AI", "fix this copy". - Skill: `itsssssjack/slopmonster` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add itsssssjack/slopmonster` - Raw SKILL.md: https://api.skillmd.com/api/skills/itsssssjack/slopmonster/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: itsssssjack (https://skillmd.com/u/itsssssjack) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/itsssssjack/slopmonster --- # SlopMonster Take any draft and make it read like a person wrote it. A landing page, a README, an email, a script. The target is not "passes a detector". Detectors are noise, and chasing them makes prose worse. The target is the gut of a reader who has seen a thousand AI paragraphs this month. The loop is always the same four steps, and the linter gets the first and last word, because the linter is honest and the model is persuasive. ``` 1. LINT python3 tools/deslop.py --text "…" score /5, exits red below 5 2. REWRITE three passes, by hand or by model (see below) 3. CLEANSE a DIFFERENT model family strips tells tools/cleanse.sh 4. RE-LINT python3 tools/deslop.py again ship only at 5/5 ``` ## Step 1 — Lint ```bash python3 tools/deslop.py page.html # a built page (scores visible text only) python3 tools/deslop.py page.html --view hero # one element by id python3 tools/deslop.py --text "paste a draft" python3 tools/deslop.py page.html --allow-proof # numbers are real and evidenced ``` Regex, no opinions. Five groups, one point each: AI vocabulary, AI constructions, punctuation cadence, rule-of-three rhythm, invented proof. Below 5/5 it exits non-zero, so it works as a build gate. "Mostly clean" is how a page ends up sounding like every other AI page on the internet. Empty input fails rather than passing. A cleanse that times out leaves a zero-byte file, and a gate that stamps that CLEAN reports slop as clean exactly when the pipeline broke. Touching a regex means running `python3 tools/test_deslop.py`. The catalogue is matched by word root, and the obvious stemming shortcut silently kills a dozen base words. ## Step 2 — Rewrite (three passes) Full catalogue in `references/signs-of-ai-writing.md`. The short version: 1. **Kill the vocabulary.** `delve`, `seamless`, `robust`, `unlock`, `elevate`, `leverage`, `game-changing`, `journey`, `realm`… Replace with a plainer word, not a synonym of the same word. 2. **Kill the shapes.** `not just X, but Y` is the single loudest tell in English right now. Also the `rule-of-three` reflex, `em-dash` pile-ups, hedge stacks, symmetrical paragraphs, the closing summary nobody asked for, and a bold lead on every bullet. 3. **Put a person back in.** Removing tells leaves clean, dead copy. One specific number per claim. Sentence lengths that vary hard. One thing a cautious writer would have cut. One rough edge — a contraction, a fragment, a sentence starting with "And". ## Step 3 — Cleanse with a rival model A model is bad at hearing its own accent. A rival model hears it instantly. So the cleanse runs on a **different model family** than the one that wrote the draft: | You are working in | The draft's accent | Cleanse with | |---|---|---| | Claude Code / Claude | Anthropic | GPT-5.6 via the codex CLI — `tools/cleanse.sh` does this | | Codex / ChatGPT | OpenAI | Claude via `claude -p`, or set `DESLOP_WRITER=gpt` for `cleanse.sh` | | Gemini CLI | Google | Either CLI; `cleanse.sh` picks whichever is installed | | No CLI at all | — | `cleanse.sh` prints the prompt; paste it into the other family's chat | ```bash tools/cleanse.sh draft.md > cleansed.md # copy out, notes on stderr tools/cleanse.sh draft.md > cleansed.md 2> notes.txt # keep the notes as well ``` The instruction it carries (`prompts/cleanse.txt`): strip the tells, keep every fact, keep the length within 10%, invent nothing. It returns **two things**. The rewritten copy, then a `<<>>` line, then up to five bullets naming each tell and its fix. The script splits them, so **stdout is copy and stderr is notes**, and the redirect above writes prose only. Read the notes. PASS 2 tells the model to stop at the last real point, so it will sometimes delete your closing line, and the notes are the only place it says so. `WARNING no <<>> line` means the model ignored the format and the whole reply came through as copy. Check the tail before you ship. ## Step 4 — Re-lint Always. A frontier model is very good at removing tells and quite capable of adding new ones while it does. An unlinted cleanse is a coin flip. ## The one hard rule **Never invent proof.** No user counts, no testimonials, no ratings, no `trusted by 10,000 teams` unless every one is true and you can show it. If a claim needs a number you do not have, write `[needs number]` and move on. The linter flags number-plus-noun patterns on purpose: a false positive costs ten seconds, a false negative is a claim you cannot back. Specificity beats borrowed credibility anyway. ## Output format Return the rewritten copy first, in full. Then a short `▎ what changed` list — at most five lines, each naming the tell and the fix. Never return analysis alone. Keep the author's meaning exactly: de-slopping is not rewriting the argument. Match the register you were given.