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-shootgit 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-shoot)<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-shoot"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-shoot/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-shoot"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-shoot.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 | $0.00089 | $0.03337 |
| Opus 5.5 | $0.00036 | $0.01335 |
| Sonnet 5.5 | $0.00018 | $0.00667 |
| Haiku 4.5 | $0.00009 | $0.00334 |
Grade A, and why
cheat-shoot 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-shoot — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-shoot — 登记拍摄完成 + 建 video folder + (改稿则) 触发 v2 预测
把视频从"已写预测、未拍摄"状态推进到"已拍摄、未发布"状态。这一步:
- 建
videos/<同 id>/目录(之前没有的话) - 询问用户:"实际拍摄时用的稿子和
scripts/<id>.md一致吗?" - 算 diff——超过 V2_TRIGGER_THRESHOLD (默认 30%) → delegate 到
/cheat-predict — mode: v2在原 prediction 文件 append## 预测 v2段 - 把 video folder 加进 state.shoots 队列,buffer +1
cheat-shoot 自己不写预测内容——所有预测落盘逻辑在 cheat-predict。cheat-shoot 只负责检测改稿 + 派发。
为什么单独一个 skill:
- buffer 警戒系统需要明确区分"拍了" vs "发了"。视频可以批量拍(一天拍 5 条),分散发(每天发 1 条)
- "实际拍摄稿" ≠ "pre-shoot 草稿"是常态。这一步是把 diff 显式化、触发 v2 重判、采集"用户改稿 pattern"信号的入口
- v2 预测 vs v1 预测的差异本身就是 rubric 升级证据——比如 v1 给 ER=4,v2 给 ER=5(用户改稿改高了 hook 强度),就告诉 rubric "这个用户的 ER 阈值跟我现在公式不一致"
Overview
[用户:拍了 scripts/2026-05-04_abc123_停止期待.md]
↓
[Phase 0: 解析路径 + 验证 prediction 已存在]
↓
[Phase 1: 检查是否已登记(避免重复)]
↓
[Phase 2: 建 videos/<id>/ + 询问"实际拍摄稿一致吗?"]
↓
[Phase 3: 写 videos/<id>/script.md]
↓
[Phase 4: append state.shoots]
↓
[Phase 5: 输出 buffer 状态]
Constants
- REQUIRE_PREDICTION = true — 拍前必须先有 v1 prediction 文件
- V2_TRIGGER_THRESHOLD = 0.30 — normalize 后 char-level diff 超过 30% → 默认建议 v2 重判;低于 30% 询问用户是否仍要 v2
- DIFF_METRIC = char_levenshtein_normalized(默认)—— 通过
tools/diff_pct.py调用:先 normalize(去 markdown header / 分隔线 / 列表标记 / 装饰标点 / 折叠所有空白),再算 char-level Levenshtein / max(len_a, len_b)。preferred backendrapidfuzz,fallbackdifflib.SequenceMatcher(stdlib,永远可用)。旧版 line-level 在口语化转录场景误报严重(draft 长 markdown 句 vs whisper 转录的短断句,内容几乎不变但 line-level 算出 ~200% diff)—— PR #14 修复 - DIFF_METRIC=lines —— legacy fallback:当 python3 完全不可用或 tools/diff_pct.py 找不到时降级到
diff -u | grep '^[+-]' | wc -l算法
Inputs
| 必填 | 来源 |
|---|---|
<scripts-path-or-id> |
用户参数;缺失则询问 |
.cheat-state.json |
状态文件 |
scripts/*.md |
pre-shoot 草稿 |
predictions/*.md |
验证对应预测存在 |
Workflow
Phase 0:解析 + 验证
- 解析用户给的路径——支持几种形态:
- 完整路径
scripts/2026-05-04_abc123_停止期待.md - 简写
2026-05-04_abc123_停止期待 - id 简写
abc123→ globscripts/*_abc123_*.md找匹配
- 完整路径
- 验证
scripts/<id>.md存在:不存在 → 报错"找不到 pre-shoot 草稿" - 验证有对应 prediction
predictions/<同名>.md:- 不存在 → 拒绝登记,提示"先跑 /cheat-predict 写预测,否则违反盲预测原则——你不能拍完才写预测,那等于事后看了画面写"
- 存在 → 通过
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 · 214 lines · 89 tokens per session scan A 42fda83dc725
cheat-shoot is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (7,309 stars, last pushed yesterday), licensed MIT. It adds 89 tokens to every session and 3,337 once invoked, about $0.0004 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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