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-recommendnpx skills add XBuilderLAB/cheat-on-content --skill cheat-recommendgit 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-recommend)<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>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 | $0.00080 | $0.03232 |
| Opus 5 | $0.00040 | $0.01616 |
| Sonnet 5 | $0.00016 | $0.00646 |
| Haiku 4.5 | $0.00008 | $0.00323 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cheat-recommend — 100% identical, 2 lines differ
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
读 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"
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.
- 4d ago First seen · 236 lines · 80 tokens per session scan A b820143508b6
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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