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-trendsgit 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-trends)<a href="https://agentmods.dev/skills/xbuilderlab/cheat-on-content/cheat-trends"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-trends/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-trends"><img src="https://agentmods.dev/badge/skills/xbuilderlab/cheat-on-content/cheat-trends.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 | $0.00105 | $0.02479 |
| Opus 5.5 | $0.00042 | $0.00992 |
| Sonnet 5.5 | $0.00021 | $0.00496 |
| Haiku 4.5 | $0.00011 | $0.00248 |
Grade A, and why
cheat-trends 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.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cheat-trends — 热点抓取
多 adapter 模式：读各 trend-sources adapter 的输出 → 去重 → 粗打分 → 写入 candidates.md。
Overview
[用户：抓热点]
↓
[Phase 0: 读 .cheat-state.json 拿 enabled adapters]
↓
[Phase 1: 对每个 adapter 调 fetch]
↓
[Phase 2: normalize 到 candidate-schema]
↓
[Phase 3: 去重（vs candidates / predictions / trends-history）]
↓
[Phase 4: 对每个新 item 粗打分（调 cheat-score 内联逻辑）]
↓
[Phase 5: 排序 + 询问用户哪些加入 candidates.md]
↓
[Phase 6: 写入 + 更新 trends-history.jsonl 缓存]
Constants
- TREND_SOURCES = ["manual-paste"] — 启用的 adapter 列表（默认仅 manual-paste，最稳）
- LOOKBACK_HOURS = 24 — 抓最近 N 小时的热点
- MAX_PER_SOURCE = 20 — 每个 adapter 最多 N 条
- DEDUPE = true — 去重开关
- AUTO_SCORE = true — 抓回来后自动调 cheat-score 粗打分
- MIN_COMPOSITE_TO_SUGGEST = 6.0 — 低于此分的不推荐用户加入候选池（仍写入 trends-history 避免下次重复推）
💡 调用时覆盖：
/cheat-trends — sources: manual-paste,aihot,weibo-hot — max-per: 10
Inputs
| 必填 | 来源 |
|---|---|
.cheat-state.json |
默认 sources |
adapters/trend-sources/<name>.md |
各 adapter 的实现描述 |
candidates.md |
去重对照 |
predictions/*.md |
去重对照（已发的不再推） |
.cheat-cache/trends-history.jsonl |
历史抓取去重缓存 |
Workflow
Phase 0: 读启用的 adapters
# 伪代码
state = read('.cheat-state.json')
enabled_adapters = args.sources or state.get('enabled_trend_sources', ['manual-paste'])
如 enabled_adapters 为空 → 输出引导：
你目前没有启用任何热点源。
最快配法：
- 临时跑：/cheat-trends — sources: manual-paste,aihot
- 永久启用：编辑 .cheat-state.json 的 enabled_trend_sources 数组
可用 adapter（详见 adapters/trend-sources/）：
- manual-paste（默认，永远能用）
- aihot（AI 热点聚合，无需 key）
- weibo-hot（微博热搜，无需 key）
- zhihu-hot（知乎热榜，无需 key）
- trendradar-mcp（TrendRadar MCP 服务，需配置）
Phase 1-2: 对每个 adapter 调 fetch + normalize
对每个 adapter，读其 adapters/trend-sources/<name>.md 中描述的 fetch 接口（实际是 Bash 调底层 Python / shell / WebFetch）：
| Adapter | 实现机制 |
|---|---|
manual-paste |
询问用户："粘贴你今天的候选 URL/标题列表（每行一条）" → 解析每行，对 URL 做 WebFetch 拓展 snippet |
aihot |
读 adapters/trend-sources/aihot.md 描述的 fetch 接口 |
weibo-hot |
读 adapters/trend-sources/weibo-hot.md 描述的 fetch 接口 |
zhihu-hot |
读 adapters/trend-sources/zhihu-hot.md 描述的 fetch 接口 |
trendradar-mcp |
读 adapters/trend-sources/trendradar-mcp.md 描述的 fetch 接口 |
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 · 200 lines · 105 tokens per session scan A 5f4e11adfcd4
cheat-trends is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (7,309 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 2,479 once invoked, about $0.0004 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.
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