whatisgithub

What is cheat-on-content?

xbuilderlab/cheat-on-content — explained in plain English

Analysis updated 2026-07-17 · repo last pushed 2026-06-27

5,255PythonAudience · writerComplexity · 2/5ActiveSetup · moderate

In one sentence

A tool for content creators that turns every post into a tracked experiment, you predict performance before publishing, then compare against real results to build a personalized formula for what works.

Mindmap

mindmap
  root((cheat-on-content))
    Inputs
      Content scripts
      Blind predictions
      Real performance data
    Outputs
      Scoring rubric
      Retrospective logs
      Personalized formula
    Use Cases
      Predict content performance
      Run post-publish retrospectives
      Refine a scoring rubric
    Tech Stack
      Python
      Claude Code
      Codex
    Audience
      Content creators
      Video creators

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Grade a script and make a blind prediction of its performance before publishing it.

USE CASE 2

Run a retrospective a few days after publishing to compare real results against your prediction.

USE CASE 3

Import 5 to 10 samples from a benchmark account to give the tool an immediate baseline.

USE CASE 4

Update your scoring rubric after missing predictions three times in a row in the same way.

What is it built with?

PythonClaude CodeCodex

How does it compare?

xbuilderlab/cheat-on-contentapple/coremltoolskarpathy/build-nanogpt
Stars5,2555,2715,305
LanguagePythonPythonPython
Last pushed2026-06-272024-08-13
MaintenanceActiveStale
Setup difficultymoderatemoderatemoderate
Complexity2/53/53/5
Audiencewriterdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires linking to an AI coding agent like Claude Code or Codex and importing benchmark samples for a baseline.

License is not stated in the available content.

So what is it?

Cheat on Content is a tool for content creators who want to stop guessing what will perform well and start treating every post like a tracked experiment. Instead of just publishing and hoping, you score and predict how a piece of content will do before it goes live. Then, a few days later, you check the actual results against your prediction. Over time, this builds a personalized formula for what works on your specific channel. The workflow follows a simple loop: you grade a script, make a blind prediction about its performance, publish it, and then run a retrospective a few days later using real data. The tool logs all of this. If you miss the mark three times in a row in the same way, it prompts you to update your scoring rubric. Importantly, any changes to your formula have to prove they are more accurate than the old one by re-scoring your history, so you can't quietly fool yourself into thinking you are improving when you are not. This is designed for individual creators, particularly those making videos or serialized content, who want a system that learns their unique style rather than offering generic advice. The README positions it as different from standard AI tools because it does not write scripts for you. Instead, it acts as a judge that remembers your past flops, your benchmark accounts, and your specific cadence, getting sharper the more you use it. Setting it up involves running an install script that links the tool into an AI coding agent like Claude Code or Codex. You initialize it in your content project folder, answer a few questions, and ideally import 5 to 10 samples from a benchmark account to give it an immediate baseline. From there, daily use is handled through simple text commands to score scripts, log shipments, and run retrospectives.

Copy-paste prompts

Prompt 1
Install cheat-on-content and link it to Claude Code in my content project folder, then import 5 samples from my benchmark account.
Prompt 2
Grade this video script with cheat-on-content and log my blind prediction before I publish it.
Prompt 3
Run a cheat-on-content retrospective on my last post using its real view and engagement numbers.
Prompt 4
Show me how cheat-on-content decides when to prompt me to update my scoring rubric after repeated misses.
Prompt 5
Explain how cheat-on-content validates that a new scoring formula is actually more accurate than my old one.

Frequently asked questions

What is cheat-on-content?

A tool for content creators that turns every post into a tracked experiment, you predict performance before publishing, then compare against real results to build a personalized formula for what works.

What language is cheat-on-content written in?

Mainly Python. The stack also includes Python, Claude Code, Codex.

Is cheat-on-content actively maintained?

Active — commit in last 30 days (last push 2026-06-27).

What license does cheat-on-content use?

License is not stated in the available content.

How hard is cheat-on-content to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is cheat-on-content for?

Mainly writer.

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