效率工具来源：GitHub 协议：MIT 协议已标注

cheat-on-content

把跨平台内容处理、改写和分发步骤做成可组合的效率工具。

6.8K GitHub stars 2026-09-05 星标同步 2026-07-16 收录时间

#content#workflow#automation

快速判断

01这个 Skill 是干嘛的: 把内容评分、改写、校准和跨平台分发做成可组合的自动化流程。
02它能解决什么问题: 解决批量内容处理标准不一致、人工反复校准和多平台适配成本高的问题。
03它适合谁来用: 适合已有内容标准、需要批量生产与质检的内容运营团队。

cheat-on-content

把跨平台内容处理、改写和分发步骤做成可组合的效率工具。

主要能力

提议并执行 rubric 或 bucket 升级。两种模式：完整 rubric bump（最高风险动作，5 步强制 + 跨模型审核）和 --bucket-only 轻量重校（只换 bucket 边界，不动 rubric 公式）。Phase 2 强制走 cheat-score-blind sub-agent 给校准池重打分——不接受 self-scored fallback。触发词："升级 rubric"/"bump rubric"/"更新公式"/"我想加一个维度"/"调整权重"/"重校桶"/"recalibrate bucket"。

使用方式

仓库级安装会保留完整目录；本次核验的入口是 skills/cheat-bump/SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

批量改写容易造成同质化，公开发布前仍需人工校准观点与语气。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

<h2 align="center">Cheat on Content</h2>

<p align="center"> <strong>English</strong>  ·  <a href="docs/README_CN.md"><strong>简体中文</strong></a> </p>

<p align="center"> For content creators — a skill that turns every post into a calibrated experiment. </p>

<p align="center"> You're reading this. The skill predicted it.<br> It turns every "I feel this will go viral" into a calibrated experiment.<br> It took me from zero to 1M followers in a month. It said I'd write this. I did.<br> Your doubt — predicted too. </p>

---

🎬 What it actually does

Most creators live in the same gambling loop:

Publish → Numbers come in → Learn nothing → Roll the dice again

A creator who's shipped 200 pieces is barely 10% sharper than someone who's shipped 1 — because they never kept books after each round.

Cheat on Content makes every judgment get logged, retrospected, absorbed into the next:

📊 Score → 🎯 Blind-predict → 🚀 Publish → 📈 T+3d retro → 🧬 Evolve your rubric

This isn't motivation. It's compounding — every piece you don't retro is silently eroding your ability to see yourself.

One month in = you have a hit-formula that's only yours. Three months in = you're 10× sharper than your first-day self.

---

🌀 Origin

I never believed in fate. Until this skill made me film a video — and predicted exactly how much traffic that video would pull. I tried to break it. I told my audience. I hoped collective observation would collapse the wave function and shift the trajectory. The data was accurate. I didn't escape fate. I just moved from first-order to second-order. If even my awakening — even my audience's observation — was already in its prediction, then right now, reading this: are you here out of curiosity, or just closing the algorithm's last move? — the creator

---

⚖️ How it differs from other "creator tools"

Others	This
Give you "inspiration"	Make your own intuition measurable
AI writes for you	AI judges for you — the script stays yours
Ship 10 versions, A/B test	Ship one — bet in writing, settle the books with data
Static dashboard	An evolving rubric — your formula 3 months from now isn't the starting one

In a sentence: other tools help you "ship more." This helps you "judge sharper."

---

🤔 Can't I just use ChatGPT / DeepSeek / Doubao?

Those are general assistants — they tell everyone the same thing. You ask "will this go viral?" and the answer is fitted to global average opinion, not your channel. Ask again tomorrow — same answer. It doesn't remember you. It doesn't change because of you.

This is your own ops expert — serving only your one channel:

The scoring formula is reverse-engineered from your history, not the global training distribution
Every piece you ship updates its understanding — by month three, judgment accuracy is 10× sharper than day one (auto-evolving)
It knows your benchmark account, your cadence, the last three reasons you flopped — things ChatGPT forgets after the first reply

General LLMs help everyone. This helps your account.

---

🛡️ Why the loop actually evolves

📝 Every piece is logged: Score and prediction get written before publish, archived end-to-end. Three days later you settle accounts — you see exactly where you were sharp, where you were off. No more vague "I feel this one didn't land."

🔁 It gets sharper: Three same-direction misses in a row, the tool actively prompts you to upgrade your scoring formula. You don't have to remember — it remembers for you.

🛡️ Upgrades have a brake: Switching the formula requires re-scoring all historical samples — only released if it ranks more accurately than the old. Plus a cross-model independent audit — so you can't fool yourself.

🪒 The rubric is a workbench, not a museum: Observations refuted by data get deleted; observations absorbed into formal dimensions also get deleted. It only holds what's most useful right now.

---

📦 Install

git clone https://github.com/XBuilderLAB/cheat-on-content.git
cd cheat-on-content
bash install.sh

⚠️ Upgrading from v0.x? Run /cheat-migrate in your content project after git pull. The 1.3 → 1.4 migration is BREAKING for blind-channel integrity — it splits rubric_notes.md so the blind sub-agent can't leak actuals. Without migrate, blind scoring will keep flagging non_blind_warning. See CHANGELOG and migrations/1.3-to-1.4.md.

14 sub-skills are symlinked into your agent's skill directory. One install, every content project gets it.

Supported agents: Claude Code (default) · Codex (bash install.sh --codex) · Both (bash install.sh --all)

Frozen version: bash install.sh --copy / bash install.sh --codex --copy Uninstall: bash uninstall.sh / bash uninstall.sh --codex (your content data is not touched)

---

🚀 First run

In your content project directory, open a skill-compatible agent and say:

初始化 cheat-on-content

(or init cheat-on-content)

Five yes/no questions complete onboarding. Strongly recommend importing a benchmark account — 5–10 samples and the tool gets an anchor immediately. Without one, your first 5 predictions land at ±50% precision.

---

⚡ Daily use

score this scripts/<...>.md       → grade only
start prediction scripts/<...>.md → blind prediction + decision log
shot scripts/<...>.md             → create video folder + buffer +1
shipped https://...                → buffer -1
retro videos/<...>/                → T+3d data + retrospective
status / fetch trends / find topic / bump rubric / find benchmark

Hook-aware agents auto-report buffer + pending retros + top candidates at every session start — no need to ask. Other agents: just say status.

Full workflow + sub-skill details: see SKILL.md.

---

📈 Star History

---

📜 License

MIT. Commercial use, modification, closed-source integration — all fine.

---

Is this cheating? So was the calculator. So was Google. The future doesn't reward effort — it rewards those who see the pattern first.

You reading this line — that's predicted too.

安装

选择适合你当前 Agent 的方式

01 · 发给当前 Agent自动识别兼容方式与安装位置

请帮我安装「cheat-on-content」。它是一个 Agent Skill 或仓库级 Skill 包。
来源：https://github.com/XBuilderLAB/cheat-on-content

请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/XBuilderLAB/cheat-on-content」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。

在 GitHub 查看原文

本站按仓库级 package 收录，安装时克隆完整 cheat-on-content 仓库；已核验的 skill 入口为 skills/cheat-bump/SKILL.md。

运行前阅读 README、依赖说明和 MIT 许可；涉及账号、外部写入或高风险领域时保留人工确认。

安装即视为你已阅读并遵守该 skill 的开源协议。内容版权归原作者所有。