slop-chop

AI writing leaves fingerprints.

Chop the slop. Paste in text and get back something that reads like a person wrote it.

Try it without installing anything: slop-chop.com runs the same engine in your browser.

AI writing has patterns. It loves em-dashes, drops a semicolon into every other sentence, reaches for words like comprehensive and leverage, and clears its throat with openers like "In summary" or "Giving it to you honestly."

slop-chop removes those patterns in a single pass. You can also hand it your own list of things to cut, so the result reads like you instead of a chatbot.

Why

Cleaning this up by hand is tedious. Asking a model to "stop using em-dashes" works for about three sentences before it forgets. slop-chop applies the same cleanup rules every time.

How it works

There are two passes, and you can run either one on its own.

The first is a rules pass. It is fast and deterministic. It swaps characters, drops words you have flagged, rewrites stock phrases and words, runs your own patterns, fixes spelling to one dialect, and tidies the punctuation, with no model, no cost, and the same output on every run. It knows markdown, so fenced code blocks and inline backtick spans come through untouched.

The second is an optional rewrite pass that hands the text to a model for the things rules cannot manage, like reworking a sentence so it no longer needs a semicolon, or nudging the writing toward a voice you picked.

Install

Homebrew:

brew install dcadolph/tap/slop-chop

With Go:

go install github.com/dcadolph/slop-chop@latest

Or clone and use the Makefile:

git clone git@github.com:dcadolph/slop-chop.git
cd slop-chop
make install     # build and install into $(go env GOPATH)/bin, version stamped
make uninstall   # remove it again

Run make with no target for the full list (build, test, cover, lint, fmt, tidy, clean).

Everywhere else

The same engine runs on many surfaces. All local and free unless noted.

Usage

# Print the cleaned text to stdout. Your file is not changed.
slop-chop fix notes.md

# Clean the file in place, like gofmt -w.
slop-chop fix -w notes.md

# Pipe text through it
echo "In summary, a robust—and seamless—result." | slop-chop fix

# Flag slop without changing anything (exits non-zero if it finds any)
slop-chop check notes.md

# Check or fix several files at once
slop-chop check docs/intro.md docs/guide.md README.md
slop-chop fix -w docs/intro.md docs/guide.md

# Enforce a spelling variant: flag or fix the other dialect
slop-chop check --dialect american notes.md
slop-chop fix --dialect british notes.md

# Overlay a built-in pack, like corporate phrasing to plain English
slop-chop fix --preset plain notes.md

# Use your own profile
slop-chop fix --profile myprofile.json notes.md

# Get findings as JSON for other tools to read
slop-chop check --json notes.md
slop-chop check --json --pretty notes.md

# Deeper clean: rules first, then a model rewrite (needs ANTHROPIC_API_KEY)
slop-chop fix --rewrite notes.md
slop-chop fix --rewrite --verify notes.md

check --json prints a {"findings": [...]} object to stdout, and fix --json adds the cleaned text as {"cleaned": "...", "findings": [...]}. Each finding carries the rule, the matched text, the suggested replacement, and a line and column.

Modes

• check flags what it finds and exits non-zero. Drop it in CI. Add --why and every finding carries a plain-words line saying what fired and what happens to it, the same explanations the web app shows on a tap.
• fix writes the cleaned text to stdout and leaves your file alone. Pass -w to change the file in place instead.
• score measures how densely the text carries the patterns your profile lists, 0 to 100. It is a lint result, not a reading on who wrote the text, and it cannot be turned into one: the same patterns appear in writing people produce on purpose. Under 25 reads clean, 25 to 54 is mixed, and 55 and up is dense with tells, the same bands the web app shows. score --by-paragraph scores each paragraph on its own, which is how a document with two generated paragraphs buried in a thousand human words shows where they are, and --max then gates on the hottest paragraph instead of the diluted whole.

Source code gets its own treatment. On a .go, .py, .ts, or any other code file, check and score read only the comments, so a buzzword in a comment is flagged at its real line and column while identifiers, strings, and formatting alignment draw nothing. fix refuses to rewrite code files outright, since prose cleanups break code. Pipe a file through stdin to override either behavior on purpose. Data files with no comments, like .json and .csv, are skipped with a note.

Score

score gives a single number from 0 to 100 for how densely the text carries the patterns the active profile lists. Change the profile and the same prose scores differently, which is the clearest statement of what the number is: compliance with a named ruleset. A structural tell counts double toward the density, because a stock sentence shape survives a thesaurus where a listed word does not.

The engine ships with a labeled corpus of AI, human, and technical passages under sanitize/testdata/, and TestBenchmark measures recall, precision, and the score margin against it on every run, so a change that weakens detection fails the build instead of going unnoticed. docs/BENCHMARK.md shows the numbers, the corpus composition, what fires on what, and the limits of what the score claims. The score is a lint result, not an authorship verdict.

slop-chop score notes.md            # notes.md: 42 (mixed: 5 tells in 180 words)
slop-chop score --json notes.md     # {"value":42,"tells":7,"words":210,...}
slop-chop score --max 20 notes.md   # exit non-zero when the score is above 20

--max turns it into a gate, so a document over the bar fails a build the same way check does.

Structural tells

Word swaps catch the vocabulary of AI writing. The rules pass also flags 61 structural tells that a word list misses: the it's not just X, it's Y cadence and its contracted isn't a perk. It's an expectation twin, the let's dive in opener, here's the thing throat-clearing, the The best part? fragment reveal, here are five ways enumeration, runs of bold-label bullets and numbered items, emoji-decorated headings, and the spaced hyphen models reach for now that the em-dash is a known tell. It also catches the register of a chat reply rather than a piece of writing: let me break this down, you might be wondering, here's where it gets interesting, happy coding!, and the say goodbye to X and Enter Foo, the tool that moves of generated marketing copy. These are flagged, not rewritten, since the fix depends on the whole sentence and is left to the rewrite pass. Add your own with the flagPatterns field in a profile.

Every tell that becomes famous gets trained out of the next model and the writing moves somewhere else, so the default profile tracks what models write now rather than what they wrote in 2023.

Use it in CI

Add a workflow that fails a pull request when it finds slop:

name: slop-chop
on: pull_request
jobs:
  slop:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: dcadolph/slop-chop@v0.41.0
        with:
          files: docs/intro.md docs/guide.md
          # profile: myprofile.json   # optional
          # dialect: american         # optional
          # preset: plain             # optional

Or have it fix the files and push the cleanup back to the pull request branch:

name: slop-chop
on: pull_request
jobs:
  slop:
    runs-on: ubuntu-latest
    permissions:
      contents: write
    steps:
      - uses: actions/checkout@v4
        with:
          ref: ${{ github.head_ref }}
      - uses: dcadolph/slop-chop@v0.41.0
        with:
          files: docs/intro.md docs/guide.md
          mode: fix
          commit: "true"
          # message: Chop the slop   # optional commit message

The fix-and-commit workflow pushes back to the pull request branch, so it works for pull requests from a branch in the same repository. A pull request from a fork gets a read-only token, so the push fails with a 403 no matter the permissions block. For fork pull requests use the check workflow above: it needs no write access and still fails the run on slop. To auto-fix your own branches, run the same fix-and-commit job on a push trigger instead.

Use it as a Claude Code plugin

An agent drafts sloppy text, slop-chop scores it 80 with 18 findings, the agent rewrites, and the revision scores 0

The loop above is the point: the agent drafts, the engine judges deterministically, the findings go back, and the rewrite is verified rather than trusted. slop-chop ships a Claude Code plugin, so the assistant can run the tool for you. The repo is its own marketplace. Add the marketplace, then install the plugin from it:

/plugin marketplace add dcadolph/slop-chop
/plugin install slop-chop@slop-chop

The slop-chop@slop-chop name is plugin@marketplace: the plugin named slop-chop, from the marketplace named slop-chop.

The plugin drives the slop-chop binary rather than replacing it, so install the binary and put it on your PATH first:

go install github.com/dcadolph/slop-chop@latest   # lands in $(go env GOPATH)/bin
slop-chop --version                                # confirm it is on PATH

The plugin then gives Claude two ways to reach the tool:

# Let the skill decide
Clean the slop out of this before I send it: <paste your text>

# Or call the command
/slop-chop notes.md

The rules pass is free. The rewrite pass needs a key and stays off unless you ask for it, so say when you want the deeper clean, and name a backend if you want a local, keyless one:

Rewrite this to sound human, and use my local Ollama so it costs nothing.

docs/PLUGIN.md is the full plugin guide, including troubleshooting.

Profiles and presets

A profile is a JSON file that lists what to cut and what to put in its place: characters, phrases, words, regular expressions, a blacklist, and a few switches. Point the tool at one with --profile, or drop a .slop-chop.json in the repo and it gets picked up on its own from any directory under it. With neither, a built-in default runs.

Presets are curated packs you overlay with --preset. The built-in packs are cleaver, plain, corporate, academic, marketing, no-dashes, and typography. cleaver is the strongest cut: the default profile only flags buzzwords like leverage and robust, and cleaver rewrites them to plain words, which is what the web app ships switched on. --preset plain turns corporate phrasing into plain English on top of whatever profile you already have, and the others target the stock phrasing of their own worlds. The last two set dash policy: no-dashes converts every dash posing as an em-dash to a comma, and typography keeps typeset dashes, curly quotes, and ellipses for text where they are deliberate. Overlay more than one with a comma: --preset corporate,plain.

docs/PROFILE.md is the full reference: every field, the presets, the spelling dialects, the allow list, and the inline ignore directives.

Attack it

slop-chop attack draft.md

attack tries to beat your own rules. It rewrites text to dodge as many as it can while leaving it no better written, then reports which tells held.

Buzzwords fall to a thesaurus: 33 of 71 word tells slip past. Sentence shapes do not, because escaping one means rebuilding the sentence, so 1 of 89 gets through. After the attack 64 of the 68 passages still carry a tell, and the mean score falls only from 85 to 79. A structural tell counts double in the score for that reason.

Run it on your own profile to find which rules a thesaurus can defeat. -w saves the evasive rewrites as a corpus. docs/BENCHMARK.md has the full table and the limits of what it proves.

Your voice

voice is the personal layer: keep protects your words from every rule and preset, prefer swaps a word for the one you would have used, and avoid flags your own crutches. slop-chop voice diff draft.md final.md reads what you changed between a draft and the version you shipped and proposes entries from your edits, which is the one voice signal a model cannot feed back to you.

slop-chop voice fingerprint posts/*.md measures the part nobody quotes: sentence rhythm, punctuation habits, and register, as numbers. slop-chop voice drift draft.md then names every trait where a text stopped sounding like you, and --bands 2 turns that into a CI gate on a house voice. Drift is not slop, so nothing is rewritten and no score moves. docs/VOICE.md has all of it.

Rewrite pass (optional)

The rules pass is deterministic and free. For the work rules cannot do, like reworking a sentence so it no longer needs a semicolon or bending the text toward your voice, add --rewrite. It runs the rules first, then hands the result to a model. It needs ANTHROPIC_API_KEY and costs money, so it stays off by default.

export ANTHROPIC_API_KEY=sk-...
slop-chop fix --rewrite notes.md
slop-chop fix --rewrite --verify notes.md

The reply is checked before you get it. The rules run over it again, its code blocks and load-bearing tokens are compared against your input, and --verify adds a model pass that flags a change in meaning. ENGINE.md covers the rewrite and its checks in full.

Backends

The rewrite pass defaults to Anthropic, but --provider openai points it at any OpenAI-compatible Chat Completions API using OPENAI_API_KEY. With --base-url you can aim that at a local server, so the rewrite runs on your own machine with no key and no cost.

# OpenAI
OPENAI_API_KEY=sk-... slop-chop fix --rewrite --provider openai --model gpt-4o notes.md

# Local Ollama, no key, no bill
slop-chop fix --rewrite --provider openai --base-url http://localhost:11434/v1 \
  --model llama3.1 notes.md

Using a different vendor to rewrite than the one that wrote the draft is a good idea, since a model is bad at spotting its own tics.

Use it as a Go library

The engine is a public package, so a Go program can chop text in process, with no binary to shell out to and nothing sent anywhere. Add it:

go get github.com/dcadolph/slop-chop/sanitize

Build a sanitizer once from a profile and reuse it. Fix returns the cleaned text and the tells it found, Check reports the tells without changing the text, and Score rates it from 0 to 100 by how densely the profile's patterns appear.

package main

import (
	"fmt"

	"github.com/dcadolph/slop-chop/sanitize"
)

func main() {
	s, err := sanitize.New(sanitize.DefaultProfile())
	if err != nil {
		panic(err)
	}
	clean, findings := s.Fix("In summary, the plan—which shipped—works; the results speak for themselves.")
	fmt.Println(clean)              // The plan, which shipped, works. The results speak for themselves.
	fmt.Println(len(findings), "tells")
}

Overlay a built-in preset with sanitize.ApplyPresets, enforce a spelling dialect through the profile's Dialect field, or fold in a personal sanitize.Voice with profile.WithVoice. The optional model rewrite lives in github.com/dcadolph/slop-chop/rewrite. The full reference is on pkg.go.dev.

Docs

Status

Still early, but the core is in place. The rules pass is built and working. The rewrite pass is built too and sits behind the --rewrite flag, because it needs an API key and costs money, so the free, predictable rules pass stays the default. The live rewrite path has a key-gated integration test, kept out of the default build so it never spends money by accident. Run it against the real API with an API key:

ANTHROPIC_API_KEY=sk-... go test -tags=integration ./rewrite/ -run Live -v

License

MIT. See LICENSE.