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-scoregit 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-score)<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-score"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-score/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-score"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-score.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 63 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00072 | $0.02240 |
| Opus 5.5 | $0.00029 | $0.00896 |
| Sonnet 5.5 | $0.00014 | $0.00448 |
| Haiku 4.5 | $0.00007 | $0.00224 |
Grade A, and why
cheat-score 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-score — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-score — 单稿打分
打分但不预测。用户用它快速看稿子的 composite，决定是否值得进入正式预测流程。
Overview
[用户：打分这篇 draft.md]
↓
[读 draft.md + rubric_notes.md]
↓
[逐维度打 0-5 + 写一行理由 + 算 composite]
↓
[控制台输出：评分 + composite + 推荐下一步]
↓
[结束 — 不写任何文件]
Constants
- RUBRIC_PATH = rubric_notes.md — 当前 rubric 来源
- OUTPUT_DETAIL = full — full: 含每维度理由；compact: 仅分数表
💡 调用时覆盖：
/cheat-score draft.md — OUTPUT_DETAIL: compact
Inputs
| 必填 | 来源 |
|---|---|
<draft-path> |
用户作为参数传入；如缺失则在对话里询问 |
rubric_notes.md |
用户项目根 |
.cheat-state.json |
用户项目根（用于读当前 rubric_version 与 mode） |
Workflow
Step 1：前置检查
- 读
.cheat-state.json→ 不存在则提示用户先跑/cheat-init，停止 - 读
<draft-path>→ 不存在或无内容 → 报错并停止 - 读
rubric_notes.md找到当前生效的公式段（一般在"当前评分维度"或"综合分公式"位置）
Step 2：识别公式与维度
从 rubric_notes.md 解析出：
- 当前 rubric_version
- 维度列表与权重（如
ER×1.5 + SR×1.5 + HP×1.5 + QL + NA + AB + SAT） - 归一化常数（如
/ 8.5 × 2.0） - 每个维度的 0-5 含义（从"当前评分维度"段表格读）
如果 rubric_notes.md 格式与预期不符（用户手改过结构）→ 询问用户当前公式是哪一行，不要自己猜。
Step 3：delegate 到 blind sub-agent（不再 inline 打分）
主对话已经被用户对话 / 已发数据 / 历史 retro 段污染——inline 打分等于带着后视镜判分。
改成通过 Task tool 调 /cheat-score-blind sub-agent，主 Claude 只做调度 + review。详见 skills/cheat-score-blind/SKILL.md。
Task prompt 模板（只能含下面这些）：
Spawn cheat-score-blind sub-agent.
Input:
script_path: <用户给的 draft path>
rubric_notes_path: rubric_notes.md
Task: 按 rubric_notes 当前公式给上面 script 打分。返回严格 JSON（见 cheat-score-blind SKILL.md Phase 2 schema）。
不要读 state file / predictions/ / videos/ 任何其他文件。
不要询问用户 —— 你没有用户。
禁止塞进 Task prompt 的东西（cheat-score-blind/SKILL.md 的"主 Claude 调用契约"段）：
- 用户对话引用 / 摘录
- 含播放数 / 万 / w / k 等字眼
- "前一次预测是 X" / "实际播放是 Y" 等 hint
- 任何
predictions/*.md路径
调用前 grep 自检：echo "<prompt>" | grep -Ei '播放|阅读|点赞|评论数|实际|retro|复盘|实绩|w$|万$' 命中 → 改 prompt 重发。
Step 4：解析 sub-agent 回传 JSON + review
sub-agent 返回严格 JSON。主 Claude：
- 解析 dimensions 段（含 score + per-dim confidence + reason）
- 校验
self_check.any_contamination_signal == false，否则警告 - 按 rubric_notes 公式算 composite（公式逻辑在主，分数来自 sub-agent）
- 不修改 sub-agent 给的维度分——score 只是显示。如果用户挑刺（"AB 给 3 不是 4"），主 Claude 记录到
User Override但 sub-agent 原始分留档
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 · 160 lines · 72 tokens per session scan A 6efb9ce47362
cheat-score is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (7,309 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 2,240 once invoked, about $0.0003 per session on Opus 5.5. 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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