Cheat on Content is a workflow for content creators that records predictions and results for each post, reviews performance later, and updates the criteria used for future decisions. It is intended to make content planning and publishing an experiment that becomes more informed over time. The catalogue contains the skills that implement this workflow.
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 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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00134 | $0.07528 |
| Opus 5.5 | $0.00054 | $0.03011 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (7,309 stars, last pushed yesterday), licensed MIT. It adds 134 tokens to every session and 7,528 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…