cheat-shoot

A workflow step for recording that a planned video has been filmed. It creates the video’s folder, saves the script used, checks whether the filmed version differs from the draft, and updates the publishing queue.

In plain words
What is it for?
Marking videos as filmed, comparing the recorded script with the draft, storing filming details, and tracking the queue of filmed but unpublished videos.
Why use it?
A video can be filmed long before it is published, and the final script may differ from the planned one. Recording this state prevents those stages from being confused and can trigger a new prediction when the change is large.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/xbuilderlab/cheat-on-content/cheat-shoot
Any agent
npx skills add XBuilderLAB/cheat-on-content --skill cheat-shoot
Clone the repo
git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content

Made for: Claude Code, Codex.

Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00089 $0.03337
Opus 5 $0.00044 $0.01669
Sonnet 5 $0.00018 $0.00667
Haiku 4.5 $0.00009 $0.00334

Measured 3d ago against content hash 42fda83dc725, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/cheat-shoot/SKILL.md · 214 lines

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:

  1. videos/<同 id>/ 目录(之前没有的话)
  2. 询问用户:"实际拍摄时用的稿子和 scripts/<id>.md 一致吗?"
  3. 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
  4. 把 video folder 加进 state.shoots 队列,buffer +1

cheat-shoot 自己写预测内容——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 backend rapidfuzz,fallback difflib.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

  1. 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 → glob scripts/*_abc123_*.md 找匹配
  2. 验证 scripts/<id>.md 存在:不存在 → 报错"找不到 pre-shoot 草稿"
  3. 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

Read the full file on GitHub · 214 lines

Changes

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.

  1. 3d ago First seen · 214 lines · 89 tokens per session scan A 42fda83dc725

Subscribe to this mod's changes

cheat-shoot is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (6,715 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 3,337 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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