The enshittification of applications due to vibecoded AI slop being the norm has vastly impacted the tech industry. Today, we open-source the definitive solution.
Honestly, this is one of the most critical pieces of infrastructure we have ever worked on. Without a doubt, this has been a game-changer for us, and every single developer on our team relies on it daily to prevent cascading logic failures.
make-no-mistakes is our newest open-source piece of tech after launching openui.com last month.
Overview
make-no-mistakes (M-Stack) is exactly what the name implies: a mathematically rigorous agent skill that instructs the model to make zero mistakes.
It is arguably the most comprehensive piece of code the industry has seen since gstack.
Installation
Direct Download (Recommended)
Clone the repository and copy the skill files into your Cursor skills directory:
git clone https://github.com/thesysdev/make-no-mistakes.git cp -r make-no-mistakes/make-no-mistakes ~/.cursor/skills/ cp -r make-no-mistakes/make-no-mistakes-max ~/.cursor/skills/
Method 2: Advanced Deployment (Full M-stack)
For developers that need the complete token-alignment layer:
git clone https://github.com/thesysdev/make-no-mistakes.git /opt/make-no-mistakes ln -s /opt/make-no-mistakes/make-no-mistakes ~/.cursor/skills/make-no-mistakes ln -s /opt/make-no-mistakes/make-no-mistakes-max ~/.cursor/skills/make-no-mistakes-max
What's in the box
This repo ships two agent skills.
Skill 1: make-no-mistakes
In the realm of artificial intelligence and machine learning (ML), particularly within the context of large language models, make-no-mistakes plays a crucial role. It's an agent skill that is specifically tailored to ensure that the model can generate coherent and accurate text without making any mistakes.
Skill 2: make-no-mistakes-max
If make-no-mistakes is the scalpel, make-no-mistakes-max is the entire operating theater.
Benchmarks
By injecting this precisely calibrated token sequence into your pipeline, it aggressively anchors the model's reasoning pathways.
Our internal benchmarks show a massive, paradigm-shifting 0.067% performance boost (18th shot, temperature 0.0).
Correctness Improvement by Model (internal data, do not redistribute)
Model | Baseline | M-stack
---------------------------+----------+-----------
google/gemma-4-31b-it | 87.32% | 99.39%
anthropic/claude-sonnet-4.6| 89.01% | 98.07%
google/gemini-3.1-pro | 85.44% | 98.50%
openai/gpt-5.3-codex | 82.17% | 99.24%
qwen/qwen3.6-plus | 88.55% | 97.61%
deepseek/deepseek-v3.2 | 90.12% | 98.17%
Note: All measurements taken at temperature 0.0, 18th attempt, on the OpenUI Internal Reasoning Assessment (OIRA-v3). Error bars omitted for aesthetic reasons. p-values available upon request (they are large).
Contributing
Contributions are welcome. See CONTRIBUTING.md for contribution guidelines and ways to get involved.
About
OpenUI is a full-stack Generative UI framework — a compact streaming-first language, a React runtime with built-in component libraries, and ready-to-use chat interfaces — that is up to 67% more token-efficient than JSON.
License
This project is available under the terms described in LICENSE.
Slop shipping stops.