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Official description7 sourcesMCP servers8d ago
manavmishra/ZeroSlop

manavmishra/ZeroSlop

Open-source Agent Skill that finds and removes AI slop from your writing
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Config template (adjust for your client)
claude mcp add zeroslop -- npx -y zeroslop
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
133 ★ · manavmishra/ZeroSlop · read from the GitHub search API · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 133 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository manavmishra/ZeroSlop;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals

Zero Slop

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Find and remove AI slop in your writing. Get rid of workslop without losing your core intent and message.
Zero Slop is a free, open-source agent skill that finds and removes AI slop while checking that the core details of your message survive the edit. If your AI setup does not support agent skills, try the browser editor or use our MCP connector.
Why it exists
An AI draft can be grammatically sound and still read like workslop. In a compatible AI assistant, Zero Slop flags slop patterns, then guides the edit. The writing score finds patterns worth reviewing; it cannot tell who wrote the text.
See an edit
Let's see Zero Slop at work. Imagine using AI to write a linkedin launch announcement and getting this:
> We're thrilled to announce that our team has leveraged cutting-edge machine learning to deliver a seamless onboarding experience, reducing setup time by 40%.
The local Python scorer in Zero Slop scores the input Slop score 99.3/100. A high Slop score means the draft is more likely to contain sloppy patterns.
Zero Slop then strips the patterns and guides the AI agent to produce the deslopped output below:
> We used machine learning to reduce onboarding setup time by 40%.
Quick start
The installed skill can make an optional, metadata-only request to GitHub’s public releases API to check for updates. It sends no draft or private learning data. Set ZS_NO_UPDATE_CHECK=1 to disable it. Hosted MCP requests are separate and are covered by our privacy policy.
You can try the browser editor without installing anything, install the skill in an assistant that supports skills, or use the hosted service through MCP and the API.
If you use Claude Code, Codex, or another assistant that supports skills, here's how to install Zero Slop there:
To see the flagged passages without an edit, use /zero-slop inspect (your writing).
The command above works with Claude Code and Codex. In Claude.ai, upload the skill ZIP. Other installation paths include Gemini CLI and remote MCP connections where your client allows them. You can also score a file locally without a model call.
What it does
Your AI assistant, whether Claude, GPT, or another compatible model, reads and edits the draft. The skill supplies the workflow and local tools: a 0 to 100 writing score, source-detail checks, and a final comparison with the original.
What it catches
The scorer uses 294 weighted patterns and a 96-term lexicon. It checks for:
- binary contrast formulas: “It's not X. It's Y.”
- canned openers: “We're thrilled to…” and “Here's the thing…”
- vague attribution: “experts agree” and “studies show”
- significance inflation: “marks a pivotal moment” and “a testament to”
- promotional wording: “robust,” “seamless,” and “leverage” when used as hype
- repeated sentence shapes, crowded statistics, and overworked formatting
The editing workflow
The Zero Slop agent uses an eight-stage workflow. Each stage is a job with a role, not a separate model; some run in the Python tools and others run in the user's AI app. We treat eight stages as an engineering convention, not eight separate models.
| Stage | Job |
|---|---|
| 1. Scorer | Find exact phrases, pacing problems, readability issues, and overworked formatting. |
| 2. Interpreter | Read the claims, audience, structure, and voice before editing. |
| 3. Rewriter | Remove stock language without inventing detail. |
| 4. Fact gate | Check names, numbers, quotations, links, code, tables, paths, and structure locally. |
Source
awesome · https://github.com/manavmishra/ZeroSlop
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