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-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/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-recommend"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-recommend.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 | $0.00080 | $0.03232 |
| Opus 5.5 | $0.00032 | $0.01293 |
| Sonnet 5.5 | $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 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-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.
- 1mo 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 (7,309 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 3,232 once invoked, about $0.0003 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…