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-predictnpx skills add XBuilderLAB/cheat-on-content --skill cheat-predictgit 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 | $0.00000 | $0.07651 |
| Opus 5 | $0.00000 | $0.03825 |
| Sonnet 5 | $0.00000 | $0.01530 |
| Haiku 4.5 | $0.00000 | $0.00765 |
Grade A, and why
cheat-predict 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-predict — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-predict — AI 主导的盲预测 + 用户 review
这个工具是"作弊器"——AI 帮你做判断。所以 cheat-predict 的核心是：
- Claude 自己读稿子 + 打 7 维分 + 给 bucket + 概率分布 + 反事实场景
- 用户 review 后回 "ok" 接受，或指出哪个维度 / 哪个判断不对
- 默认走快路径：用户直接 ok → 落盘
- 慢路径：用户挑刺某个维度 → Claude 改 → 再 review → 直至确认
不是用户从 7 维分到概率分布全部自己写，那 Claude 只剩"格式化器"——失去工具的核心价值。
严格遵守 shared-references/blind-prediction-protocol.md——见过任何后续数据就不能写预测，只能记 reconstructed。 完整组件清单见 shared-references/prediction-anatomy.md。 Confidence 派生表见 shared-references/state-management.md。
Overview
[用户：启动预测 scripts/<id>.md]
↓
[Phase 0: blind check 自检] ← 触犯就拒绝
↓
[Phase 0.7: 模式判定 — v1 (新建) 还是 v2 (append)]
↓
[Phase 1: 读 script + rubric + state + 派生 confidence]
↓
[Phase 2: **委派 cheat-score-blind sub-agent**（Task tool）拿 9 维盲打 + per-dim confidence]
↓
[Phase 2.5: 主 Claude 对 blind 输出做 review — 若任意维度 |delta| ≥ 2 vs 主估，弹给用户裁定]
↓
[Phase 3: **Claude 自己**找锚点对比]
↓
[Phase 4: **Claude 自己**给 bucket + 概率分布 + 中枢] ← confidence 低时分布更平
↓
[Phase 5: **Claude 自己**写反事实场景 + 关键校准假设]
↓
[Phase 5.5: **用户 review**——展示完整草拟版，等用户 "ok" 或挑刺]
↓
├─ "ok" → Phase 6 落盘
└─ "X 维度应该 Y 不是 Z" → Claude 改 → 再 review → 循环
↓
[Phase 6: 落盘 — v1 写新文件 / v2 append 到现有文件 ## 复盘 之前]
↓
[Phase 7: 更新 state.in_progress_session]
Constants
- SCRIPTS_DIR = scripts/ — 草稿源目录
- PREDICTION_DIR = predictions/ — 落盘目录
- BLIND_CHECK = strict — strict（默认）/ lenient（仅警告，不推荐）—— 跟 blind-prediction-protocol.md "见过数据"边界相关
- BLIND_SCORING = on（默认）/ off —— 是否走 cheat-score-blind sub-agent。off 等价于
--skip-blindflag，标last_prediction_self_scored: true给 cheat-status 警告 - DISAGREEMENT_THRESHOLD = 2 —— blind 与主 Claude 自评的单维度差异 |Δ| ≥ 此值 → Phase 2.5 弹用户裁定
- BUCKET_PRESET = auto — 自动派生：有 baseline_plays → 按 baseline × {0.3 / 1 / 3 / 10 / 30}；无 baseline → 平台通用默认
- MIN_ANCHORS = 2 — 锚点对比期望 2 个；不够时显式标"锚点 N/A"段（不删段，不省略）
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 · 453 lines · 0 tokens per session scan A 9e1096e853bb
cheat-predict is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (6,715 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 7,651 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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