zero-slop
Find and fix canned AI writing while preserving the facts, voice, and format.
Open source Open in the app JSON README (API)
About
Find and fix canned AI writing while preserving the facts, voice, and format.
Details
- Kind
- Plugins
- Topic
- No topic detected
- Publisher
- manavmishra
- Origin
- gemini
- Category
- ferramentas
- Version
- 2.9.2
- Added
- 2026-09-07 14:38:11
- Updated
- 2026-09-07 14:38:11
- Origin id
manavmishra/zeroslop
README
# Zero Slop
<p align="center">
<img alt="MIT" src="https://img.shields.io/badge/license-MIT-202521">
<img alt="tests" src="https://img.shields.io/badge/tests-passing-227B5B">
<img alt="dependencies" src="https://img.shields.io/badge/runtime%20dependencies-0-227B5B">
<img alt="privacy" src="https://img.shields.io/badge/learning-private-227B5B">
<img alt="version" src="https://img.shields.io/badge/version-2.8.10-72528F">
<a href="https://hol.org/guard/plugins"><img alt="Listed in the HOL plugin registry" src="https://img.shields.io/badge/HOL%20registry-listed-2C6E8F"></a>
<a href="https://github.com/hashgraph-online/awesome-ai-plugins#tools--integrations"><img alt="Listed in awesome-ai-plugins" src="https://img.shields.io/badge/awesome--ai--plugins-listed-2C6E8F"></a>
<a href="https://zero-slop.ai/try/"><img alt="This README scores 11.6 out of 100 on the slop score, inside the human range" src="https://img.shields.io/badge/slop%20score-11.6%2F100-0f7d55"></a>
</p>
Less slop, more pop in your writing.
Zero Slop finds stock phrasing, mechanical rhythm, vague claims, and canned
formatting. Local checks run offline; your existing AI assistant does the editing.
Score a draft at [zero-slop.ai/try](https://zero-slop.ai/try/); benchmark at
[zero-slop.ai](https://zero-slop.ai).
```sh
npx zero-slop install
```
### What it does, on a real draft
A launch post, as AI wrote it:
> We're thrilled to announce that our team has leveraged cutting-edge AI to deliver a seamless onboarding experience. It's not just a redesign, it's a fundamental reimagining of how users engage with our platform. In today's fast-paced landscape, the ability to iterate quickly is crucial. By harnessing the power of machine learning, we've been able to reduce setup time by 40%. Here's the thing nobody tells you: onboarding is where most products lose their users. We're excited to continue this journey with you.
`slopscore.py --explain` on that paragraph, unedited:
```text
Writing score: 100.0/100 [major rewrite]
Flagged phrases : 11 across 83 words
Sentence variety: too even
Main issues : canned LinkedIn phrase, promotional language, manufactured
stakes, two-part contrast used as a formula, performed
writer's voice, buzzword used as promotion
Flagged phrases (11), strongest first:
"In today's fast-paced" manufactured stakes; start where the reader needs to start
"We're thrilled to" canned LinkedIn phrase; say what happened without the stock opening
"It's not just a redesign, it's"
two-part contrast used as a formula; state the claim once, plainly
"Here's the thing" performed writer's voice; say the thing plainly
'cutting-edge' promotional language; name what it does; cut the adjectives
'leveraged' buzzword used as promotion; use the plain word
```
The rewrite, limited to the draft's stated claims:
> We used machine learning to reduce onboarding setup time by 40%.
```text
Writing score: 9.5/100 [clear]
Flagged phrases : 0 across 10 words
```
## Problem
AI-assisted writing often converges on the same constructions: "It's not X. It's Y."
"Here's the thing nobody tells you." Repeated wording, uniform rhythm, and
overworked formatting can have the same effect.
Zero Slop is an Agent Skill and ships no model. Claude, GPT, or another compatible
model edits; local tools protect names, numbers, quotations, links, code, tables,
and paths. Your AI compares meaning that word matching cannot judge.
## How to install Zero Slop
Paste this into Claude Code, Codex, Cursor, OpenCode, Warp, or Zed:
```text
Install the Zero Slop skill globally from https://github.com/manavmishra/ZeroSlop
```
Or install it with `npx`:
```sh
npx skills add manavmishra/ZeroSlop --global
```
Or install from npm, which also adds the scoring command:
```sh
npx zero-slop install # add --harness codex|cursor|opencode|zed
npx zero-slop score draft.md # score without installing anything
```
ChatGPT users can download [`dist/zero-slop-single-file.md`](dist/zero-slop-single-file.md).
Claude.ai users can upload the [release ZIP](https://github.com/manavmishra/ZeroSlop/releases/latest/download/zero-slop.zip).
## How to use Zero Slop
```text
/zero-slop (your writing)
```
You get the edited draft, before-and-after scores, and quoted flagged phrases.
`/zero-slop inspect (your writing)` reviews without rewriting. For a folder,
`slopscore.py --batch drafts/ --gate 25` fails above the threshold.
## Show your score
The badge above is this README's own, from `npx zero-slop score README.md`.
Put yours up the same way:
```md
[](https://zero-slop.ai/try/)
```
Colours are the scorer's bands: `0f7d55` under 25, `b8860b` under 60, `b0502c`
above.
## The slop that Zero Slop catches
294 weighted patterns and a 96-term lexicon, including:
1. Binary contrasts: "It's not X. It's Y."
2. Throat-clearing openers: "Here's the thing," "Let me be clear"
3. Faux-insight setups: "What nobody tells you," "The part everyone misses"
4. Colon reveals: "The best part: it learns."
5. Dramatic fragments: "That's it. That's the whole thing."
6. Superficial analysis: "highlighting the team's commitment to innovation"
7. Importance puffery: "marks a pivotal moment," "a testament to"
8. Weasel attribution: "experts agree," "studies show"
9. Synonym cycling: the agent, the assistant, the tool, all one thing.
10. Marketing riders: "robust" and "leverage" score only beside a marketing trigger, so a runbook stays quiet.
A reading pass covers repeated shapes, crowded statistics, and paragraphs that
shuffle without loss. [`references/eval.md`](references/eval.md) has all 80 checks.
Human writing scored 9 to 21 in [`data/corpus/must-not-flag/`](data/corpus/must-not-flag/);
unedited AI drafts averaged 77 across [`bench/examples.json`](bench/examples.json).
## How it works
Eight responsibilities form one workflow. They are jobs, not separate models. An
installed assistant can isolate them.
The free web editor combines
the five AI responsibilities into one model response, then runs the scorer and source
checks locally. It makes at most one live model call and never describes that response
as independent review. The research supports the checks, not the number eight, which
is an engineering choice.
| Role | Who does it | What happens |
|---|---|---|
| 1. Scorer | Local tools | Finds the exact phrases behind the writing score, then checks pacing and readability. It also catches overworked formatting. |
| 2. Interpreter | Your AI assistant | Reads the claims, purpose, audience, structure, and voice before changing anything. |
| 3. Rewriter | Your AI assistant | Removes stock language and rebuilds order, rhythm, and tone without inventing detail. |
| 4. Fact gate | Local tools | Rejects any version that changes names, numbers, quotations, links, code, tables, paths, or structure. |
| 5. Copy desk | Your AI assistant | Corrects grammar, spelling, usage, and consistency in the actual deliverable. |
| 6. Read-aloud editor | Your AI assistant | Fixes stumbles, repetition, weak transitions, and awkward flow. |
| 7. Verifier | Local tools and your AI assistant | Compares text with source for facts, meaning, qualifiers, voice, format, structure. In the web editor, source safety is checked locally. |
| 8. Fresh-eyes finalizer | Your AI assistant | Reads the finished text as a first-time reader and applies only safe polish. Any change receives one final local recheck. |
Missed editorial targets do not erase a useful edit or start an open-ended loop. Zero
Slop permits one targeted repair and one local recheck, then returns the safest
source-preserving edit with a plain warning. On the website, a failed model call falls
back to the same conservative local editor shipped with the installed skill instead of
retrying through more providers. Shared fixtures require the browser and local command
to return the same fallback edit, character for character.
Studies find
[predictable wording](https://arxiv.org/abs/2301.11305) and
[overused vocabulary](https://arxiv.org/abs/2406.07016) in machine text, and authorship
detectors can [misclassify non-native English](https://arxiv.org/abs/2304.02819). Local
tools use only Python's standard library.
## Private learning from your edits
Learning starts only when you provide the original output and your edited version.
Zero Slop does not monitor files, browsers, or publishing tools.
Private data stays under `$ZERO_SLOP_HOME`.
This human-in-the-loop learning never retrains the model. A profile selected by name
can exempt existing watchlist words; it does not learn cadence, tone, or a complete
style.
## What's inside
[`SKILL.md`](SKILL.md) has the workflow and [`references/eval.md`](references/eval.md) the
80 checks. [`scripts/slopscore.py`](scripts/slopscore.py) is the meter and fact gate,
with [`scripts/register.py`](scripts/register.py) running the reading pass.
[`bench/README.md`](bench/README.md) documents every benchmark with its limits.
[zero-slop.ai](https://zero-slop.ai) has the same reference as browsable pages, plus the [benchmark in full](https://zero-slop.ai/benchmark/).
## Testing and limits
### Against other tools, same model, same drafts
The saved replay ran Zero Slop, [avoid-ai-writing](https://github.com/conorbronsdon/avoid-ai-writing),
[no-ai-slop](https://github.com/petergyang/no-ai-slop) and
[humanizer](https://github.com/blader/humanizer) over 18 drafts with GPT-5.4, high
reasoning, and pinned instructions. Zero Slop's outputs came from v2.5.9; later
releases only rescore those frozen outputs.
| Method | Mean writing score ↓ | Passed Zero Slop's local gates | Source check passed | Average length change |
|---|---:|---:|---:|---:|
| Original drafts | 76.3 | 0/18 | — | — |
| Zero Slop | 12.8 | 18/18 | 18/18 | -8.9% |
| avoid-ai-writing | 23.3 | 15/18 | 18/18 | -14.6% |
| no-ai-slop | 28.4 | 12/18 | 17/18 | -13.7% |
| humanizer | 35.4 | 9/18 | 17/18 | -7.2% |
Two cross-checks: AIStoryHub clean rates and a method-hidden quality ranking.
This small LLM-reviewed regression study measures neither field accuracy nor a
universal ranking. Drafts, hashes, and limits are in [`bench/README.md`](bench/README.md).
The separate two-way replay used Zero Slop v2.6.0 and is preserved in
[`bench/incumbent-blind-replay/`](bench/incumbent-blind-replay/).
For the 38-item editorial panel, the current scorer matched the prior 84.2% result.
All frozen scores stayed unchanged, all 18 human controls remained below the gate,
and all 18 obvious search cases remained above it. These fixed-sample checks are not
proof of general accuracy.
### Speed
One Apple silicon Mac: the meter scored 1,000 documents in 1.9929 seconds
(501.8 per second), 15,201 words in 0.3223 seconds, and the worst stress case in 2.2932
seconds. The reading pass handled the same 1,000 in 0.7823 seconds (1278.3/second);
learning on 8,000 words took 0.1592 seconds. Across 24 interleaved runs against 2.7.7,
median throughput was 0.26% lower, effectively no difference. Editing is excluded.
### Reading-pass accuracy
The reading pass budgets antithesis pairs by frequency, so the count has to be right
before the budget means anything. On 75 labelled pairs in
[`bench/antithesis/`](bench/antithesis/):
| Reading pass | 2.8.0 | now |
|---|---:|---:|
| Recall, all shapes | 40.0% | 91.2% |
| Recall, shapes in reach | 44.4% | 100% |
| Precision | 80.0% | 100% |
| False positives | 3 | 0 |
2.8.3 added the families the 58-pair corpus never tested, where 2.8.2 scores 67.5%
precision.
Bare subject swap and the weak isocolon stay out of reach and count against recall: both
are identical to ordinary prose on every lexical statistic. Maintainer labels on
constructed pairs, so this is a regression floor, not field accuracy.
### Current models
The pinned [RAID+](https://huggingface.co/datasets/markstanl/RAID-Plus) sample yielded
7,627 usable generations:
| Model | Texts scored | Mean writing score ↓ | At or above 25 |
|---|---:|---:|---:|
| DeepSeek V3 | 1,995 | 14.5 | 10.1% |
| Gemini 3.1 Pro | 1,998 | 17.0 | 18.2% |
| Gemma 3 27B | 1,634 | 21.6 | 30.4% |
| Llama 3.3 70B | 2,000 | 25.5 | 41.7% |
RAID+ labels capture which model produced a text, not how well it reads. In
Beemo, raw responses averaged
30.2, expert edits 25.3, human answers 20.0. Neither dataset has quality labels.
## Where Zero Slop came from
Zero Slop builds on work by no-ai-slop, humanizer, de-slop, stop-slop, unslop-text,
and avoid-ai-writing. It adds a writing score, source protection, separate editorial
passes, private learning, portfolio analysis, and release tests.
The chart says nothing about writing quality. Reproduce it with:
```sh
python3 tests/test_all.py
python3 scripts/calibrate.py --selftest
python3 scripts/register.py --selftest
python3 bench/make_charts.py --check
```
## License
MIT