Cheat on Content is a workflow for content creators that records predictions and results for each post, reviews performance later, and updates the criteria used for future decisions. It is intended to make content planning and publishing an experiment that becomes more informed over time. The catalogue contains the skills that implement this workflow.
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 skills add XBuilderLAB/cheat-on-content --skill cheat-retrogit clone --depth 1 https://github.com/XBuilderLAB/cheat-on-contentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-retro)<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-retro"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-retro/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-retro"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-retro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.06248 |
| Opus 5.5 | $0.00000 | $0.02499 |
| Sonnet 5.5 | $0.00000 | $0.01250 |
| Haiku 4.5 | $0.00000 | $0.00625 |
Grade A, and why
cheat-retro 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 1mo 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-retro — 86% identical, 24 lines differ
How it starts
The opening of the file, as written. The whole thing — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-retro — 数据回收与复盘
抓 T+N 天的实际表现 → 对比预测 → 提炼新观察 → 写入 rubric-memo.md。只追加 ## 复盘 段,绝不改预测段。rubric_notes.md 是 blind 白名单,只能保存通用公式、维度定义和抽象规则,不能写入样本名、实绩、评论、链接或播放/阅读数。
Overview
[用户:复盘 predictions/2026-05-04_...]
↓
[Phase 0: 校验 immutability + 校验时间窗口]
↓
[Phase 1: 抓数据(manual paste 或 adapter)]
↓
[Phase 2: 写实绩段 + top 评论关键词]
↓
[Phase 3: 验证/推翻预测的各假设]
↓
[Phase 4: 提炼新观察]
↓
[Phase 5: 落盘(追加到 ## 复盘 段)]
↓
[Phase 6: 写入 rubric-memo.md 的"观察记录"段]
↓
[Phase 7: 检测是否触发 bump 候选 → 提示用户跑 /cheat-bump]
Constants
- RETRO_WINDOW_DAYS = 3 — 默认 T+3d。短视频快平台可设 1,长文设 7
- DATA_SOURCE = manual — manual: 用户粘数字;adapter: 调对应平台 adapter(需配置)
- AUTO_PROPOSE_BUMP = true — Claude 判断是否系统性偏差时自动提议 /cheat-bump
- 默认参考:连续 ≥3 次同向偏差(high/low)→ 提议
- 但 Claude 可以更早提议:1 次极端偏差(如中枢 50w 实绩 5w 这种 ≥10x),即使没有"连续"也提议
- 也可以更晚:3 次同向但每次偏差都很小(<25%),可能只是噪声不是系统性
- TOP_COMMENTS_N = 20 — 抓 / 粘 top N 高赞评论
💡 调用时覆盖:
/cheat-retro <file> — window: 7 — source: adapter
Inputs
| 必填 | 来源 |
|---|---|
<prediction-file> 或 <video-folder> |
用户参数;缺失则从 .cheat-state.json 的 pending_retros[0] |
rubric_notes.md |
用户项目根(只读,用于当前规则上下文;不得写入实绩观察) |
rubric-memo.md |
用户项目根(写入复盘观察、实绩证据、样本名与评论信号) |
.cheat-state.json |
状态文件 |
入参解析(同 cheat-predict 双形态接受)
用户给的可能是:
predictions/2026-05-04_<id>_<short>.md→ 直接用这个 prediction 文件videos/2026-05-04_<id>_<short>/→ 找对应的 prediction 文件(按 id 匹配)+ 把 report.md 写到该 video folder 里- 缺省 → 从
pending_retros[0]取最早的
Workflow
Phase 0: 校验
- 读
<prediction-file>,确认存在 - 识别有效预测段:扫所有
## 预测...段(可能含## 预测、## 预测 v1、## 预测 v2等):- 取最后一个
## 预测 vN作为本次校准的依据(v2 存在则用 v2;只有 v1 则用 v1;legacy 单段## 预测直接用) - state.shoots 对应项的
v2_prediction_written应与"是否存在 v2 段"一致——不一致则警告(state 与文件脱节)
- 取最后一个
- 校验 immutability:在内存 cache 住所有
## 预测...段的内容(用于 Phase 5 后核对——全部段不可改,不只是有效段) - 校验文件 header 有
Published at→ 没登记的不能复盘,提示用户先/cheat-publish - 校验时间窗口:今天 - published_at >= RETRO_WINDOW_DAYS。不够 → 提示"还差 X 天",询问用户是否仍坚持复盘(标
early_retro: true) - 校验已有复盘段是否已填——已填则询问"是补充还是修正?"
- 补充 → 在已有复盘段下追加新子段,标日期
- 修正预测段(用户错觉)→ 拒绝
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.
- 1mo ago First seen · 380 lines · 0 tokens per session scan A e4857a971fac
cheat-retro is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (7,309 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 6,248 tokens. 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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