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-seednpx skills add XBuilderLAB/cheat-on-content --skill cheat-seedgit 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-seed)<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-seed"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-seed.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 | $0.00134 | $0.07528 |
| Opus 5 | $0.00067 | $0.03764 |
| Sonnet 5 | $0.00027 | $0.01506 |
| Haiku 4.5 | $0.00013 | $0.00753 |
Grade A, and why
cheat-seed 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cheat-seed — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-seed — 选题对话（默认）/ 批量 brainstorm（可选）
cheat-seed 的核心是跟用户讨论选题，不是机械地 brainstorm。好内容来自用户的真实经历 + 观察 + 情绪——这些是 AI 不可能凭空 brainstorm 出来的。AI 的角色是听用户讲 → 帮提炼角度 → 写一份 draft，不是 dump 15 候选让用户挑。
默认模式：对话式一次一个。
Batch 模式（--batch N）：保留旧的 brainstorm N 候选 + 写 N 份 draft 流程，给"完全没想法 + 想批量初始化"的用户。
三种 Mode（自动识别）
Mode A — 用户主动给主题（**最常见**）：
用户："/cheat-seed" + 直接说"我想做一条关于 X 的"
或："/cheat-seed 我最近开会被领导..."
↓
AI 围绕 X / 这件事**深挖**——什么瞬间触发？最让你 [情绪 / 不爽 / 觉得有意思] 的是哪点？
↓
收敛到一个具体角度 → 提议 → 用户认可 → 写 1 份 draft → 完成
↓
问"下一篇？" 或用户说"今天就这样"
Mode B — 用户给方向但不具体：
用户："最近想做点关于 [职场 / 婚恋 / AI / ...] 的"
↓
AI："[范围] 太广。最近你接触到的具体哪件事让你想做这个方向？"
↓
收敛到 Mode A 的具体经历
Mode C — 用户完全没想法（少见）：
用户："我不知道做什么" / "帮我想个题"
↓
AI："好，进 brainstorm 模式——先抓热点 + 你之前的兴趣方向，给你 1 个建议"
↓
跑 trend-sources 抓热点 + 读 candidates.md / predictions/ 看用户历史
↓
提议 1 个角度（不是 5 个） → 用户认可 → 写 draft
Batch Mode — 用户显式要批量（`/cheat-seed --batch 5`）：
按旧版 brainstorm 流程：3 问题 → 15 候选 → 用户挑 → 写 5 draft。
给"今天想一次性把未来 2 周的选题搞定"的用户。
关键纠正（与旧版的区别）：
- AI 不主动开放问——等用户给输入再深挖
- 一次一个选题，不是 5 个
- 默认对话式 + 一次一个，batch 是 escape hatch
Constants
- DEFAULT_TREND_SOURCES = ["manual-paste"] — 仅 Mode C / Mode A 灰色场景 / Batch 用到。用户可在 state 里加 aihot / trendradar-mcp
- TREND_TOOL_ROUTING — 按
content_form路由数据源，详见 shared-references/data-source-routing.md - MODE_B_MAX_REPROBE_TURNS = 2 — Mode B "为什么" 反问最多 2 轮；超过则转 Mode C
- MAX_DEEP_DIVE_TURNS = 4 — Mode A 收敛阶段最多 4 轮反问，避免 AI 过度盘问
- WITH_DRAFT = yes — 默认确认角度后立刻写 draft；用户可说 "等下，我自己写" 跳过
- DRAFT_LENGTH — 派生自
state.typical_duration_seconds：30s→100-200字 / 90s→250-500字 / 240s→600-1000字 / 450s→1100-2000字 / 900s→2200+字 - HUMANIZE_DRAFT = on（默认）/ off —— 写完 draft 后用
humanizerskill 过一遍，去掉 AI 写作 tells（em-dash 滥用 / rule of three / inflated 词汇 / 空泛归因等）。off 时直接出原始 AI draft。只 humanize 正文，不动 header 的"必须改写"警告
Inputs
| 必填 | 来源 |
|---|---|
.cheat-state.json |
读 calibration_samples / typical_duration / cadence |
rubric_notes.md |
读当前 rubric（粗打分用） |
script_patterns.md |
读已有 pattern（写 draft 时按 cheat sheet 选结构） |
predictions/*.md（如有） |
已发历史，brainstorm 时作为 context |
audience.md（如有） |
受众画像——选题 / 写稿时的"谁在看"镜子（由 /cheat-persona 派生） |
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.
- 3d ago First seen · 433 lines · 134 tokens per session scan A afd7eeac337a
cheat-seed is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (6,715 stars, last pushed 3d ago), licensed MIT. It adds 134 tokens to every session and 7,528 once invoked, about $0.0007 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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