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.
npx agentmods add skills/xbuilderlab/cheat-on-content/cheat-initnpx skills add XBuilderLAB/cheat-on-content --skill cheat-initgit clone --depth 1 https://github.com/XBuilderLAB/cheat-on-contentWhat 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.
| Model | Per session | Once 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 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cheat-init — 86% identical, 33 lines differ
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
- 读用户当前工作目录(用户的 content project,不是 cheat-on-content 自己)
- 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
- 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,不批量提问)
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。
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.
- 2d ago First seen · 537 lines · 131 tokens per session scan A e9a32b6bd130
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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