# XBuilderLAB/cheat-on-content

You're reading this. The skill predicted it. A workflow that turns every post into a calibrated experiment—score, blind-predict, retro, evolve. The future doesn't reward effort, it rewards those who see the pattern first. 1M followers in a month — not luck, system.

Repository: https://github.com/XBuilderLAB/cheat-on-content
Canonical: https://ross.abutalabs.com/products/cheat-on-content
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-24T03:42:57+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 8
- inputs: {"age_days": 120, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6620, forks 907 (observed 2026-08-28T04:09:45.727199+00:00)

## What it is
A Python-based agent skill that turns content creation into a calibrated prediction workflow: score each post, blind-predict its performance, publish, run a T+3-day retrospective, and evolve a personal scoring rubric. It is aimed at content creators who want to systematically improve their intuition about what content performs well.

## Use cases
- predict whether a post will go viral before publishing
- run retrospectives on social media post performance
- build a personal content scoring rubric that improves over time
- track and calibrate my content creation intuition
- systematically learn why some posts outperform others
- replace guesswork in content strategy with logged predictions

## When to choose
- you publish content regularly and want a structured predict-then-retro loop
- you want to quantify and improve your own judgment rather than outsource writing to AI
- you prefer a compounding personal rubric over generic creator dashboards

## When to avoid
- you want an AI tool that writes or generates content for you
- you need A/B testing or multi-variant experimentation infrastructure
- you expect a polished GUI dashboard rather than a skill-driven workflow
- you are skeptical of marketing-heavy framing and want a battle-tested enterprise tool

## Facets
- artifact type: plugin
- maturity: active
- function: analytics, workflow-automation, data-science, prompt-engineering
- domain: social-media, analytics, developer-tools, artificial-intelligence
- platform: python, cli, cross-platform
- tags: content-creation, creator-economy, calibrated-prediction, retrospective-workflow, agent-skill, viral-forecasting, rubric-evolution, social-media-analytics, automation

## Member repositories
- XBuilderLAB/cheat-on-content (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.727199+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:43:18.162673+00:00, confidence not recorded.
  - readme: https://github.com/XBuilderLAB/cheat-on-content (fetched 2026-08-28T04:09:45.727199+00:00, sha 0f4262c7403e)
- Data as of 2026-08-30T08:39:29.467469+00:00.