cheat-recommend

cheat-recommend is a skill for Claude Code, Codex from XBuilderLAB/cheat-on-content. It costs 80 tokens per session (3,232 once invoked), scanned A, original, MIT.

A topic-selection workflow that reads a candidate list and recommends the highest-ranked video ideas. It uses existing scores, publication history, rejected ideas, safety filters, and recent topics to avoid duplicates.

In plain words
What is it for?
Finding the next video topic, requesting a chosen number of recommendations, filtering candidates, excluding published or rejected ideas, and balancing stable topics with experiments.
Why use it?
Choosing the next topic manually can overlook scores, previously published material, or recent repetition. The workflow applies the project’s existing selection rules consistently.

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

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cheat-recommend

README.md
[![agentmods](https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-recommend.svg)](https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-recommend)
Your own site
<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-recommend"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-recommend.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,232 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 $0.00080 $0.03232
Opus 5 $0.00040 $0.01616
Sonnet 5 $0.00016 $0.00646
Haiku 4.5 $0.00008 $0.00323

Measured 4d ago against content hash b820143508b6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cheat-recommend 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 4d 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-recommend/SKILL.md · 236 lines

How it starts

The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/cheat-recommend — 候选池排序推荐

读 candidates.md → 按 composite 排序 → 输出 top N 推荐,每条带评分细节 + 锚点对比 + 推荐理由。

Overview

[用户:推荐选题]
  ↓
[Phase 0: 检查 candidates.md 存在性]   ← 不存在则引导,不报错
  ↓
[Phase 1: 解析 candidates 列表]
  ↓
[Phase 2: 过滤(tier / 安全性 / 已发过)]
  ↓
[Phase 3: 排序 by composite + 找锚点]
  ↓
[Phase 4: 输出 top N + 每条的 rationale + 锚点对比]

Constants

  • TOP_N = 5 — 默认推荐 top 5
  • STRATEGY = stable+experimental — 推 ≥2 时按 cadence-protocol.md 的"1 稳分 + 1 实验性"策略;推 1 时只推 top 稳分
  • POOL_PATH = candidates.md — 候选池路径
  • EXCLUDE_PUBLISHED = true — 排除已发布的(与 predictions/*.md 去重)
  • EXCLUDE_REJECTED = true — 排除用户主动跳过的(tier=skip
  • REQUIRE_SCORED = true — 只推荐已打分的——避免推没读过的素材
  • DUPLICATE_CATEGORY_LOOKBACK — 派生自 state.target_publish_cadence_days:max(3, cadence_days × 3) 天内已发同类目候选不推(避免审美疲劳)

💡 调用时覆盖:/cheat-recommend — top: 3 — filter: safe

Inputs

必填 来源
candidates.md 用户项目根
predictions/*.md 用于去重
.cheat-state.json 当前 rubric_version

Workflow

Phase 0: 候选池存在性检查

candidates.md

状态 处理
文件不存在 不报错。输出引导:见下方"无候选池引导"
文件存在但空(< 1 个 entry) 同上
文件存在且非空 进入 Phase 1

无候选池引导(核心:不让用户第一次遇到 cheat-recommend 时被劝退):

你目前没有候选池(candidates.md 不存在或为空)。

绝大部分人没有候选池——这很正常。四个建立方式,挑一个:

1. 🌱 [推荐] 跑 /cheat-seed
   一次性的种子动作:3 个问题(兴趣 / 调性 / 红线)→ 拉公开热点 + Claude brainstorm
   → 输出 15 候选让你挑 5 → 默认顺带写 5 个 draft。5 分钟搞定。
   
   - 没发过历史的:纯 brainstorm(兴趣 × 热点)
   - 发过历史的(init 时已 import):brainstorm 会基于"你过去做过什么"给推荐
   
   说:"找选题" 或 "seed"

2. 🔥 [日常补充] 用 /cheat-trends 抓 20 条带打分的候选
   说:"抓热点" — 从 weibo-hot / zhihu-hot / b站热门 / HN / 你配的源各拉 N 条
   适合已经跑过 /cheat-seed、想日常补充候选池的用户

3. ✍️  手动建:把候选标题贴进 candidates.md,每行一条
   我会自动给每条粗打分

4. 📋 从 Notion / RSS 导入:跑 /cheat-init --mode add-pool 配置 adapter

你也可以跳过候选池,直接给我具体稿子说"启动预测"。

> /cheat-seed vs /cheat-trends 的区别:
> - seed 是种子动作(含 brainstorm + 可选 draft),适合"我从零开始没选题"
> - trends 是日常多 adapter 抓取(不 brainstorm 不写 draft),适合"日常补充候选池"

Read the full file on GitHub · 236 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. 4d ago First seen · 236 lines · 80 tokens per session scan A b820143508b6

Subscribe to this mod's changes

cheat-recommend is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (6,749 stars, last pushed 3d ago), licensed MIT. It adds 80 tokens to every session and 3,232 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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