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-learn-fromgit 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-learn-from)<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-learn-from"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-learn-from/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-learn-from"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-learn-from.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 | $0.04532 |
| Opus 5.5 | $0.00000 | $0.01813 |
| Sonnet 5.5 | $0.00000 | $0.00906 |
| Haiku 4.5 | $0.00000 | $0.00453 |
Grade A, and why
cheat-learn-from 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-learn-from — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 400 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-learn-from — 对标账号导入
工具早期最重要的信号源是对标账号——你 init 完没数据,rubric 等权 v0 等于占星。但如果你能找一个你想做成那样的账号,导入 5-10 条它的高/中/低样本,工具就有了 anchor。
后期当你自己 calibration_samples ≥ 10 时,benchmark 影响自然减弱——你的真实数据成为主要信号源。但 benchmark.md 不删,仍是 cheat-seed brainstorm 的 reference frame。
Overview
[用户:学这个账号 / 启动 cheat-learn-from]
↓
[Phase 0: 检查 benchmark 状态]
↓
[Phase 1: 选 input 方式(Way a 默认)]
↓
[Phase 2: 收集材料]
Way a: 用户粘 N 条 script 文本 + 数据
Way b: 用 whisper 转录 samples/ 目录里的视频
↓
[Phase 3: 询问每条样本的"印象判断"(高/中/低 + 为啥)]
↓
[Phase 4: Claude 拆 pattern + 派生 rubric 信号]
↓
[Phase 5: 用户 review → 改 → 落盘]
↓
[Phase 6: 写 benchmark.md / script_patterns.md / rubric_notes.md]
↓
[Phase 7: 更新 state.benchmark_status]
Constants
- MIN_SAMPLES = 3 — 最少 3 条样本(少于拆不出 pattern)
- RECOMMENDED_SAMPLES = 5-10 — 推荐区间,平衡信号量 vs 用户工作量
- MAX_SAMPLES_PER_RUN = 15 — 单次导入上限——再多 Claude context 不够 + 用户也累
- DEFAULT_WAY = a — Way a 简单 + 准确,是 default
Inputs
| 必填 | 来源 |
|---|---|
<账号名> |
用户参数;缺失则询问 |
.cheat-state.json |
状态文件 |
| Way a: 用户粘的 script 文本 + 数据 | 对话 |
Way b: samples/<账号名>/*.mp4 等视频文件 |
用户提前下载好放进去 |
Workflow
Phase 0: 检查 benchmark 状态
读 .cheat-state.json 的 benchmark_status:
| 状态 | 处理 |
|---|---|
none |
首次导入——继续 Phase 1 |
pending |
用户之前答应等下找——继续 Phase 1 |
imported 已有 benchmark |
询问"你已有 benchmark [当前名],N 条样本。要做什么? a) 追加新视频到当前 benchmark b) 替换为新 benchmark c) 只看不改" |
参数解析:
--append→ 追加到现有 benchmark--replace <new-name>→ 用新 benchmark 替换(旧的归档到 benchmark.archived/)- 没标志 + 已有 benchmark → 走上面询问
Phase 1: 选 input 方式(两个独立维度)
每条样本 = script + 数据。两者怎么拿是独立的——你可以混搭。
Phase 1a: script source(怎么拿稿子)
script 怎么拿?
a) **粘文本(最简单,推荐)**
- 你自己整理过 / 用工具提取过——直接粘进对话
- 工具推荐(按方便程度排):
抖音 / 小红书:
- 微信小程序「轻抖」—— 粘视频链接 → 自动提取文案 + 评论。最快
- 类似工具:"视频解析助手" / "短视频文案提取" 等小程序
- 通常有免费额度,重度使用收费
B 站 / YouTube:
- 视频页面有"显示字幕/文字记录"按钮(如果 UP 主开了)
- 第三方:DownSub / SaveSubs / yt-dlp --write-auto-sub
公众号 / Substack:
- 直接复制网页文字
b) **whisper 转录视频文件**
- 你下载了视频到 samples/<账号名>/<video>/source.mp4
- 需要装 whisper-cpp + ffmpeg(见 adapters/script-extraction/whisper/README.md)
- 转录可能有错别字 / 漏字 / 标点不准——准确度比 a 差
c) **跳过 script,只用元数据 + 印象**
- 你拿不到稿子也懒得用工具
- 后果:pattern 拆不深(只能看标题 / 数据 / 你的印象),但 rubric 信号还行
- 适合"先快速搭起来,将来补"
回 a / b / c。
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 · 400 lines · 0 tokens per session scan A ca9aa5d7b8f1
cheat-learn-from 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 4,532 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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