## File: README.md
Cheat on Content
---
## 🎬 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
```bash
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](CHANGELOG.md) and [migrations/1.3-to-1.4.md](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](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.*
---
## File: adapters/script-extraction/whisper/README.md
# Adapter: whisper（视频/音频转录）
被 `/cheat-learn-from` 在 Way b（用户提供视频文件，让工具转录）时调用。
> **优先 Way a**（用户直接粘 script 文本——简单 + 准确）。Way b（whisper）只在用户**找不到 script 只有视频**时用。
---
## 这个 adapter 是干嘛的
把 mp4 / mov / mp3 等媒体文件转成文字 transcript，让 Claude 能读对标账号的稿子。
抖音 / B站 / YouTube 大多数视频**没有官方字幕**——拿稿子绕不开 ASR（语音转录）。这是为什么本 adapter 存在。
---
## 安装（一次性）
### 选项 A：whisper-cpp（**推荐**——快、轻、纯 C++）
Mac M 系列芯片上一条 3 分钟视频转录 30-60 秒。
```bash
# 1. 装 whisper-cpp
brew install whisper-cpp
# 2. 装 ffmpeg（whisper-cpp 依赖，从视频里抽音频）
brew install ffmpeg
# 3. 下载模型（中文推荐 medium 或 large-v3，准确度够 + 速度还行）
# whisper-cpp 第一次运行会自动下载，或手动：
mkdir -p ~/.whisper-cpp/models
cd ~/.whisper-cpp/models
# medium 模型 (~1.5GB)
curl -L -O https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-medium.bin
```
### 选项 B：openai-whisper（Python 版，更慢但有 API 兼容性）
```bash
pip install openai-whisper
brew install ffmpeg
# 模型自动下载
```
### 选项 C：用云端 API（不需要本地模型）
`/cheat-learn-from` 暂不直接支持云端 API——如果你有 OpenAI / Azure / 阿里云的 ASR API key，可以自己改 `run.sh` 走云端。
---
## 用法
cheat-learn-from 自动调用，你不需要手动跑。但如果想手动测试：
```bash
# 转录单个视频
bash run.sh
# 例：
bash run.sh ~/Desktop/对标账号/某视频.mp4 ~/my-channel/samples/对标账号/abc123/
# → 输出 ~/my-channel/samples/对标账号/abc123/transcript.md
```
## 输出格式
`transcript.md`：
```markdown
# Transcript: