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-scorenpx skills add XBuilderLAB/cheat-on-content --skill cheat-scoregit 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 | $0.00072 | $0.02240 |
| Opus 5 | $0.00036 | $0.01120 |
| Sonnet 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 3d 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.
- 3d 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 (6,715 stars, last pushed 2d ago), licensed MIT. It adds 72 tokens to every session and 2,240 once invoked, about $0.0004 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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