cheat-init

An onboarding workflow for setting up a content-prediction project from scratch. It asks about the project, creates its folders and templates, and checks whether its automation hooks work.

In plain words
What is it for?
It initializes a new project, optionally imports a user's previous publishing history, creates scripts and prediction folders, tests hooks, and provides a list of what to say next.
Why use it?
It removes the guesswork from initial setup and helps users begin with the right files, settings, and next steps.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/xbuilderlab/cheat-on-content/cheat-init
Any agent
npx skills add XBuilderLAB/cheat-on-content --skill cheat-init
Clone the repo
git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content

Made for: Claude Code, Codex.

Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,877 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00131 $0.07877
Opus 5 $0.00066 $0.03939
Sonnet 5 $0.00026 $0.01575
Haiku 4.5 $0.00013 $0.00788

Measured 2d ago against content hash e9a32b6bd130, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cheat-init scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/cheat-init/SKILL.md · 537 lines

How it starts

The opening of the file, as written. The whole thing — 537 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/cheat-init — 首次 onboarding

让用户从零到能跑第一篇预测,全程 ≤ 5 分钟(没发过历史的)或 ≤ 10 分钟(已发过、要 import 历史的)。

Overview

[用户首次说"初始化"]
  ↓
[Phase 0: 检测当前状态]
  ↓
[Phase 1: 首屏文案 — 适用性 + 期望管理]
  ↓
[Phase 2: 6 个问题(Q1-Q5 都问;Q2 决定是否走 user-history import)]
  ↓
[Phase 2.5: 对标账号 — 强烈建议(cold-start 必须问,已发用户可选)]
  ↓
[Phase 3: 创建脚手架(含 scripts/ + videos/ + samples/ 空目录 + 模板文件含 benchmark.md)]
  ↓
[Phase 3.5: user-history import 流程(仅 Q2=有发过历史 + 用户同意)]
  ↓
[Phase 4: 测试 hook 是否生效]
  ↓
[Phase 5: 给"下一步该说什么"清单]

Constants

  • DEFAULT_RETRO_WINDOW_DAYS = 3
  • INSTALL_HOOKS = ask — 默认询问;用户选 auto 直接装;skip 不装
  • TREND_DEFAULT_SOURCES = ["manual-paste"]

Inputs

无。所有信息从 6 个对话问题里收集。

Workflow

Phase 0: 检测当前状态

  1. 读用户当前工作目录(用户的 content project,不是 cheat-on-content 自己
  2. 检查是否已存在 .cheat-state.json
    • 存在 → 提示"项目似乎已初始化(state file 存在)。要重新初始化会覆盖现有配置——确认?" 等用户明确确认才继续
    • 不存在 → 进入 Phase 1
  3. 检查是否已存在 rubric_notes.md / predictions/ 等核心文件——存在但 state file 不存在 → 是"半初始化"状态,提示用户并询问"要从现有文件推断状态还是重置?"

Phase 1: 首屏直白告知期望(含适用性验证)

向用户输出(一字不漏,不要软化):

🎯 Cheat on Content / 网红外挂 — 初始化

你的下一条内容已经在改写 3 个月后的你。
规律是客观存在的,区别是你**看见**还是**没看见**。
这套让你看见。

接下来 5-10 分钟我会问你 5-6 个问题搞清楚你做什么、有什么、怎么用。
两件事先说在前面:

1. **早期预测会不准**——前 5 篇精度大概 ±50%,这是数学事实。
   工具用 🔴🟠🟡🟢🔵 标 confidence 等级,不藏数字——
   你自己判断这次能不能信。

2. **强烈建议导对标账号**——5-10 条对标视频,工具立刻有 anchor。
   不然第一批预测基本是占星。后面 Q5 会再问一次。

准备好开始吗?

如果用户答"继续"或类似肯定回应 → Phase 2。 不再因为 content_form 拒绝继续——任何形态都允许,只是 rubric_form_mismatch 字段标真,cheat-status 后续会持续提示用户"你的形态需要 bump 调权重"。

Phase 2: 6 个问题(一问一答,批量提问)

Q1: 内容形态

"你的内容更接近哪一种? a) 观点视频(评论 / 时评 / 论说 / 议题讨论 / 个人观点)— 直接匹配内置 rubric b) 长文 essay(公众号 / Substack / Medium)— 可借观点视频 rubric 起步,bump 时调权重 c) 短文 / thread(X / 微博 / 即刻)— 同上 d) 播客 / 视频长内容(YouTube 长片 / 播客)— 同上 e) 教程 / 工具教学 / Builder(教别人怎么用 X 工具 / 怎么做 Y 项目)— 同上 f) 其他(游戏 / 美食 / 妆教 / 新闻 / 剧情)— 工作流通用,但 rubric 维度需要调 (ER / SR / HP 这套对你形态可能不太预测,需要自己拆出适合的维度) g) 混合"

记录到 content_form + rubric_form_mismatch

Read the full file on GitHub · 537 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 537 lines · 131 tokens per session scan A e9a32b6bd130

Subscribe to this mod's changes

cheat-init is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (6,715 stars, last pushed 2d ago), licensed MIT. It adds 131 tokens to every session and 7,877 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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