Agent skill

Cheat Shoot

by XBuilderLAB in XBuilderLAB/cheat-on-content

登记一条视频已拍摄。建 video folder + 询问实际拍摄稿是否与 scripts/<id.md 一致 + buffer +1。与 cheat-publish 配对:拍了进队列,发了出队列。触发词:"拍了"/"拍了 X"/"shot"/"shot it"/"已拍 X"/"录完了"。

MITAuto-check: notesEducation

Install Cheat Shoot

skills CLI
$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-shoot -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install XBuilderLAB/cheat-on-content cheat-shoot --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cheat-shoot .claude/skills/cheat-shoot && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cheat-shoot
GitHub stars
7.2k
Token cost
~1.9k tokens
SKILL.md length
483 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

登记一条视频已拍摄。建 video folder + 询问实际拍摄稿是否与 scripts/<id.md 一致 + buffer +1。与 cheat-publish 配对:拍了进队列,发了出队列。触发词:"拍了"/"拍了 X"/"shot"/"shot it"/"已拍 X"/"录完了"。

  • Works in 6 steps: :解析 + 验证 → :检查重复 → :建 video folder + 询问稿子一致性 → …
  • Education work in your project
  • SKILL.md covers Overview, Constants, Inputs and Workflow, plus 4 more sections
  • Calls python3

What it does

Cheat Shoot is an agent skill from XBuilderLAB/cheat-on-content. 登记一条视频已拍摄。建 video folder + 询问实际拍摄稿是否与 scripts/<id.md 一致 + buffer +1。与 cheat-publish 配对:拍了进队列,发了出队列。触发词:"拍了"/"拍了 X"/"shot"/"shot it"/"已拍 X"/"录完了"。

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education. The repository describes itself as: You're reading this. The skill predicted it. A workflow that turns every post into a calibrated experiment—score, blind-predict, retro, evolve. The future doesn't reward effort… The licence is MIT.

When your agent uses it

  • Education work in your project

Example prompts

  • “shot it”
  • “/cheat-shoot”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash(*), Read, Write, Edit, Glob

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. :解析 + 验证
  2. :检查重复
  3. :建 video folder + 询问稿子一致性
  4. :写 videos//script.md + (b 路径) 触发 v2 预测
  5. :state 更新
  6. :输出 buffer 状态

What it can do on your machine

Read from SKILL.md and the folder at commit 2d8211e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(*)
    • Read
    • Write
    • Edit
    • Glob

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cheat Shoot loads about 1.9k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 483 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash(*), Read, Write, Edit, Glob

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from XBuilderLAB/cheat-on-content at commit 2d8211e, republished under its MIT licence (© XBuilderLAB). 483 words, ~1,852 tokens.

Download SKILL.mdSave it as .claude/skills/cheat-shoot/SKILL.md (or your agent's skills folder).
name
cheat-shoot
description
登记一条视频已拍摄。**建 video folder + 询问实际拍摄稿是否与 scripts/<id>.md 一致 + buffer +1**。与 cheat-publish 配对:拍了进队列,发了出队列。触发词:"拍了"/"拍了 X"/"shot"/"shot it"/"已拍 X"/"录完了"。
allowed-tools
Bash(*), Read, Write, Edit, Glob
argument-hint
<scripts-path-or-id>

/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. 建 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 段
  4. 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 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/*.mdpre-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
Phase 1:检查重复

读 .cheat-state.json,检查 shoots[] 是否已含此 id:

  • 已存在 → 警告"已登记过(X 天前)。是要重新登记,还是要用 /cheat-publish 发布?"
  • 不存在 → 进入 Phase 2
Phase 2:建 video folder + 询问稿子一致性
  1. 建目录 videos/<id>_<short>/(同 scripts/ + predictions/ 的命名)
  2. 询问用户:
拍 「<title>」 的时候,你实际用的稿子和 scripts/<id>.md 一致吗?

a) 一致——按草稿拍的
b) 改了一些——你能给我看下实际拍摄稿吗?我重新打分一次(v2 预测)
c) 大改了,基本是另一条 → 走 _redo 流程:
   scripts/<id>_redo.md → 重新 cheat-predict → 再 cheat-shoot(原 prediction 留档脱钩)
Phase 3:写 videos/<id>/script.md + (b 路径) 触发 v2 预测

a 路径(一致):

  • cp scripts/<id>.md → videos/<id>/script.md
  • script_consistency = consistent
  • 不重判,进 Phase 4

b 路径(改了):

  1. 询问用户实际拍摄稿——粘贴文本 / 文件路径 / 转录文件

  2. 若用户提供 → 写入 videos/<id>/script.md

  3. 若用户没保留(即兴)→ 标 script_lost,写占位文件 + 警告"v2 重判跳过——下次建议留稿(哪怕 voice memo 转录)",进 Phase 4

  4. 提供了的话:算 diff

    bash
    # 解析 cheat-on-content 源码根(cheat-shoot 是 symlink 装的)
    SKILL_REAL="$(readlink -f ~/.claude/skills/cheat-shoot 2>/dev/null || readlink ~/.claude/skills/cheat-shoot 2>/dev/null)"
    if [[ -n "$SKILL_REAL" ]]; then
      REPO_ROOT="$(cd "$SKILL_REAL/../.." && pwd)"
      DIFF_TOOL="$REPO_ROOT/tools/diff_pct.py"
    fi
    
    if [[ -n "${DIFF_TOOL:-}" && -f "$DIFF_TOOL" ]] && command -v python3 >/dev/null 2>&1; then
      # 默认 char-level Levenshtein on normalized text(rapidfuzz preferred, difflib fallback)
      diff_pct=$(python3 "$DIFF_TOOL" "scripts/<id>.md" "videos/<id>/script.md")
    else
      # legacy line-level fallback——只在 python3 或 diff_pct.py 都不可用时用
      added=$(diff -u scripts/<id>.md videos/<id>/script.md | grep -c '^+')
      removed=$(diff -u scripts/<id>.md videos/<id>/script.md | grep -c '^-')
      total_orig=$(wc -l < scripts/<id>.md)
      diff_pct=$(( (added + removed) * 100 / total_orig ))
      echo "⚠️  fallback 到 line-level diff——口语化转录会 inflate diff_pct,可能误触发 v2"
    fi

    为什么 normalize + char-level:line-level diff 在创作者真实场景(draft 是 markdown 长句、拍摄稿是 whisper 转录的口语化短行)算出 ~200% 差异但内容几乎不变。char-level Levenshtein 在 normalize 后稳定反映内容差异,而非格式差异。详见 tools/diff_pct.py + tools/diff_pct_test.sh(3 fixture 在两个 backend 上全过)。

  5. 判定 v2 触发:

    • diff_pct >= 30 → 默认建议 v2 重判,主动调用 /cheat-predict — mode: v2 — prediction-file: predictions/<id>.md 传 videos/<id>/script.md 作 input。cheat-predict 走 v2 模式 append ## 预测 v2
    • diff_pct < 30 → 询问用户:"只改了 N% 的内容,要重判吗?默认不(v1 预测仍有效)"。用户说要 → 同上调用;用户说不 → 跳过 v2,继续 Phase 4
  6. cheat-predict 完成 v2 落盘后,控制权回到 cheat-shoot 进 Phase 4

Show full SKILL.md (146 more words)Show less

c 路径(大改):

  • 不写 videos/<id>/script.md,提示走 _redo 流程
  • 退出 cheat-shoot(不进 Phase 4)
Phase 4:state 更新
json
{
  "shoots": [
    ...,
    {
      "video_folder": "videos/2026-05-04_abc123_停止期待/",
      "prediction_file": "predictions/2026-05-04_abc123_停止期待.md",
      "scripts_path": "scripts/2026-05-04_abc123_停止期待.md",
      "shot_at": "<ISO timestamp>",
      "script_consistency": "consistent" | "modified" | "lost",
      "script_diff_pct": <0-100 int 或 null>,
      "v2_prediction_written": <true/false>,
      "script_hash_at_shoot": "<sha256:12 of videos/<id>/script.md>"
    }
  ]
}

v2_prediction_written: true 表示 prediction 文件里现在有 ## 预测 v2 段,cheat-retro 应读 v2 算偏差;false 表示沿用 v1。

Phase 5:输出 buffer 状态

读完 state 后立即算 buffer + 颜色(按 cadence-protocol.md 的派生规则):

✅ 已登记拍摄:videos/2026-05-04_abc123_停止期待/
   预测文件:predictions/2026-05-04_abc123_停止期待.md

📦 当前 buffer:3 篇(🟢 绿色,正常)
   按你的 cadence(隔日更)= 6 天 buffer,节奏稳定。

下一步:拍其他候选 / 等下个发布日 / 不动

如果 buffer 颜色变了(如从绿到蓝)→ 高亮提醒:

📦 当前 buffer:6 篇(🔵 蓝色,**积压**)
⚠️  建议暂停拍摄,全力发布存货 + 复盘。
   按你的 cadence(日更)= 6 天预备,已超过健康上限。

Key Rules

  1. 不写 prediction——拍了 ≠ 发了。预测在 /cheat-predict 锁,拍只是事件
  2. 不动 video folder 内容——script.md / draft-v0.md 都不改
  3. 必须先有 prediction——否则违反盲预测(拍完看了画面再写预测 = 数据泄漏到判断)
  4. buffer 计算实时——每次 shoot / publish 后立刻重算,state.shoots 是真值
  5. 支持批量:用户可以一天连说 "拍了 X / 拍了 Y / 拍了 Z" 三次连续登记

Refusals

  • 「拍了 X,但我从来没跑过 cheat-predict」 → 拒绝。v1 预测必须拍前写——拍完才写预测会被画面诱导事后修改。请先 /cheat-predict 写 v1 再来 /cheat-shoot。(v2 重判是另一回事——v1 已存在 + 拍后改稿才允许)
  • 「我没有 video folder,我直接拍的」 → 询问用户 → 帮他建 video folder + 提示下次走完整流程;登记时标 ad_hoc: true
  • 「我改稿了但你直接覆盖 v1 吧,别留 v2 段」 → 拒绝。v1 是档案,v2 才是当前判断——append 不覆盖。两段一起留是 rubric 学习的关键证据

Integration

  • 上游:/cheat-predict 写完 prediction → 用户拍摄 → /cheat-shoot 登记
  • 下游:/cheat-publish 发布时把对应项从 state.shoots 移除
  • /cheat-status 看板的 buffer 数字直接来自 state.shoots.length
  • /cheat-recommend 看 buffer 颜色调推荐策略
  • SessionStart hook 看 buffer 颜色决定报告第一行

state.shoots 数据结构

json
{
  "shoots": [
    {
      "video_folder": "videos/2026-05-04_abc123_停止期待/",
      "prediction_file": "predictions/2026-05-04_abc123_停止期待.md",
      "shot_at": "2026-05-04T18:30:00+08:00",
      "ad_hoc": false  // true if user shot without going through full flow
    }
  ]
}

按 shot_at 升序——最早拍的在前面。/cheat-status 显示最早一项的 days-since-shoot 警告(避免有视频拍了 30 天没发)。

© XBuilderLAB, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/cheat-shoot of XBuilderLAB/cheat-on-content.

Open the folder on GitHubat commit 2d8211e

Compare with similar skills

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Zhang Xuefeng Perspectivealchaincyf/zhangxuefeng-skill10k1 repos~2.6kAutomated safety check: PassMIT
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Categories

Questions about Cheat Shoot

What does Cheat Shoot do?

登记一条视频已拍摄。建 video folder + 询问实际拍摄稿是否与 scripts/<id.md 一致 + buffer +1。与 cheat-publish 配对:拍了进队列,发了出队列。触发词:"拍了"/"拍了 X"/"shot"/"shot it"/"已拍 X"/"录完了"。. Cheat Shoot is an agent skill from XBuilderLAB/cheat-on-content.

When should I use Cheat Shoot?

Cheat Shoot fits situations like: education work in your project.

How do I install Cheat Shoot in Claude Code?

Run `npx skills add XBuilderLAB/cheat-on-content --skill cheat-shoot -a claude-code`. Or copy the skill folder (skills/cheat-shoot in XBuilderLAB/cheat-on-content) into .claude/skills/cheat-shoot in your project. Claude Code loads it when a task matches its description.

How do I install Cheat Shoot in Codex?

Run `npx skills add XBuilderLAB/cheat-on-content --skill cheat-shoot -a codex`. Or copy the skill folder (skills/cheat-shoot in XBuilderLAB/cheat-on-content) into .agents/skills/cheat-shoot in your project. Codex loads it when a task matches its description.

Can I use Cheat Shoot in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add XBuilderLAB/cheat-on-content --skill cheat-shoot -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cheat-shoot, .gemini/skills/cheat-shoot, .github/skills/cheat-shoot and .opencode/skills/cheat-shoot in your project.

What does Cheat Shoot need to run?

Going by SKILL.md and its folder, Cheat Shoot needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, Glob.

Does Cheat Shoot access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cheat Shoot safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Cheat Shoot use?

Cheat Shoot is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cheat Shoot use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cheat Shoot?

Skills that share tags, products or a category with Cheat Shoot: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), Zhang Xuefeng Perspective (alchaincyf/zhangxuefeng-skill, 10k stars), Deep Reading Analyst (ginobefun/deep-reading-analyst-skill, 353 stars) and AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cheat Shoot?

XBuilderLAB (a GitHub organization) maintains it in XBuilderLAB/cheat-on-content, which has 7,225 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 5, 2026.

Source: XBuilderLAB/cheat-on-content on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.