Agent skill

Cheat Trends

by XBuilderLAB in XBuilderLAB/cheat-on-content

从配置的热点源（HN / Reddit / YouTube trending / B 站热门 / 等）抓今天的热门话题，去重 + 粗打分 + 写入 candidates.md。绝大部分人没有候选池——这是让"我没素材"问题在 onboarding 第二步就消失的钥匙。触发词："抓热点"/"fetch trends"/"今天有什么可做的"/"trending now"/"找选题"。

MITAuto-check: notesEducation

Install Cheat Trends

skills CLI

$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install XBuilderLAB/cheat-on-content cheat-trends --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cheat-trends .claude/skills/cheat-trends && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "cheat-trends" agent skill from https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends into .claude/skills/cheat-trends/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cheat-trends", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install XBuilderLAB/cheat-on-content cheat-trends --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cheat-trends .agents/skills/cheat-trends && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "cheat-trends" agent skill from https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends into .agents/skills/cheat-trends/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cheat-trends", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install XBuilderLAB/cheat-on-content cheat-trends --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cheat-trends .cursor/skills/cheat-trends && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "cheat-trends" agent skill from https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends into .cursor/skills/cheat-trends/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cheat-trends", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/XBuilderLAB/cheat-on-content.git --path skills/cheat-trends

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install XBuilderLAB/cheat-on-content cheat-trends --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cheat-trends .gemini/skills/cheat-trends && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "cheat-trends" agent skill from https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends into .gemini/skills/cheat-trends/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cheat-trends", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install XBuilderLAB/cheat-on-content cheat-trends

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cheat-trends .github/skills/cheat-trends && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "cheat-trends" agent skill from https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends into .github/skills/cheat-trends/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cheat-trends", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install XBuilderLAB/cheat-on-content cheat-trends --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cheat-trends .opencode/skills/cheat-trends && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "cheat-trends" agent skill from https://github.com/XBuilderLAB/cheat-on-content/tree/main/skills/cheat-trends into .opencode/skills/cheat-trends/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cheat-trends", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: cheat-trends
GitHub stars: 7.2k
Token cost: ~1.4k tokens
SKILL.md length: 369 words
Files: 1
Skills in repo: 16
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Works in 7 steps: 读启用的 adapters → 2: 对每个 adapter 调 fetch + normalize → 去重 → …
Education work in your project
SKILL.md covers Overview, Constants, Inputs and Workflow, plus 4 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cheat Trends is an agent skill from XBuilderLAB/cheat-on-content. 从配置的热点源（HN / Reddit / YouTube trending / B 站热门 / 等）抓今天的热门话题，去重 + 粗打分 + 写入 candidates.md。绝大部分人没有候选池——这是让"我没素材"问题在 onboarding 第二步就消失的钥匙。触发词："抓热点"/"fetch trends"/"今天有什么可做的"/"trending now"/"找选题"。

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education. It works with Reddit and YouTube. The repository describes itself as: You're reading this. The skill predicted it. A workflow that turns every post into a calibrated experiment—score, blind-predict, retro, evolve. The future doesn't reward effort… The licence is MIT.

When your agent uses it

Education work in your project

Example prompts

“问题在 onboarding 第二步就消失的钥匙。触发词：”
“fetch trends”
“今天有什么可做的”
“/cheat-trends”

Requirements

Python 3
Pre-approved tools (allowed-tools): Bash(*), Read, Write, Edit, Glob, WebFetch, Skill

Workflow steps

7 steps, taken from the step headings in SKILL.md.

读启用的 adapters
2: 对每个 adapter 调 fetch + normalize
去重
粗打分
排序 + 询问
落盘
状态更新

What it can do on your machine

Read from SKILL.md and the folder at commit 2d8211e. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Bash(*)
- Read
- Write
- Edit
- Glob
- WebFetch
- Skill
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python, jsonl and json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cheat Trends loads about 1.4k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 369 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~52
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
```
allowed-tools: Bash(*), Read, Write, Edit, Glob, WebFetch, Skill
```

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from XBuilderLAB/cheat-on-content at commit 2d8211e, republished under its MIT licence (© XBuilderLAB). 369 words, ~1,443 tokens.

Download SKILL.mdSave it as .claude/skills/cheat-trends/SKILL.md (or your agent's skills folder).

name: cheat-trends
description: 从配置的热点源（HN / Reddit / YouTube trending / B 站热门 / 等）抓今天的热门话题，去重 + 粗打分 + 写入 candidates.md。**绝大部分人没有候选池——这是让"我没素材"问题在 onboarding 第二步就消失的钥匙**。触发词："抓热点"/"fetch trends"/"今天有什么可做的"/"trending now"/"找选题"。
allowed-tools: Bash(*), Read, Write, Edit, Glob, WebFetch, Skill
argument-hint: [— sources: <comma-separated>] [— max-per: 20]

/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

python

# 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

每个 adapter 输出符合 candidate-schema.md 的 items。

优雅降级：单 adapter 失败（API key 缺失 / 端点 503 / cookie 失效）→ skip 该 adapter，不抛异常，在汇总里说明：

✅ aihot: 拉到 18 条
✅ weibo-hot: 拉到 15 条
✅ zhihu-hot: 拉到 12 条
⚠️  trendradar-mcp: 跳过（MCP 服务未配置——配置见 adapters/trend-sources/trendradar-mcp.md）

Phase 3: 去重

按 candidate-schema.md 的"去重协议"：

对每个 item 算 id（sha256(source_type + normalized_title + url_path)[:12]）
检查 candidates.md 已含此 id → 跳过
检查 predictions/*.md 已含此 id → 跳过
检查 .cheat-cache/trends-history.jsonl 已含此 id 且 rejected_at != null → 跳过

去重统计写到汇总报告里。

Phase 4: 粗打分

AUTO_SCORE=true 时，对每条新 item：

用 item 的 snapshot_text 作为输入
按当前 rubric 给 7 维打分（不调 /cheat-score 子 skill 走 IO；inline 复用打分逻辑）
算 composite
给一句 rationale

注意：粗打分 ≠ 正式预测。预测必须基于最终稿（用户改过的），这里的打分只是"是否值得展开写"的粗筛。

AUTO_SCORE=false 时，items 写入 candidates.md 时 composite=null，需要后续手动 /cheat-score。

Phase 5: 排序 + 询问

按 composite 降序，过滤掉 composite < MIN_COMPOSITE_TO_SUGGEST 的：

🔥 抓热点完成。各源拉取统计：
- manual-paste: 5 条（用户输入）
- aihot: 18 条
- weibo-hot: 15 条
跳过 trendradar-mcp（MCP 服务未配置）

去重后剩 27 条新 item。
粗打分后 12 条 composite ≥ 6.0：

| # | 标题 | source | composite | bucket | rationale |
|---|---|---|---|---|---|
| 1 | 为什么我们都讨厌主动联系朋友 | aihot | 8.4 | 30-100w | ER+QL 双 5，AB 普适 |
| 2 | "她不一样"的一千种变体 | weibo-hot | 8.1 | 30-100w | MS 候选维度高 |
| 3 | ...... |

哪些加入 candidates.md？
- 全部加 → 回 "all"
- 选几个 → 回 "1, 3, 5"
- 都不要 → 回 "none"（这些会被记到 trends-history 避免下次重复推）

Show full SKILL.md (148 more words)Show less

Phase 6: 落盘

用户响应后：

选中的 items → 按 candidate-schema.md 的"Markdown 表示"格式追加到 candidates.md

所有抓回来的 items（不管选中与否）→ append 到 .cheat-cache/trends-history.jsonl：

jsonl

{"id": "...", "title": "...", "source": "...", "snapshot_at": "...", "rejected_at": null|"<ISO>", "fetched_at": "<ISO>"}

Phase 7: 状态更新

json

{
  "last_trends_run_at": "<ISO>",
  "last_trends_added_count": 5
}

Key Rules

不抛异常。单 adapter 失败 → skip + 报告。多 adapter 全失败 → 报错"所有源都失败"，附排查指引
manual-paste 永远在。即使其他所有 adapter 都坏了，manual-paste 模式必须能跑——它是兜底
去重是硬约束。同 id 不重复推；用户拒绝过的 6 个月内不再推
粗打分要诚实标注。在 candidates.md 的 entry 里标 composite (rough, snapshot-based)，避免与 prediction 的精打分混淆
不直接进 predictions/。trends 只产 candidates，predict 是另一个动作

Refusals

「直接抓抖音热门 feed，不用 cookie」 → 拒绝。抖音反爬极严，无 cookie 必失败；引导到 douyin-session adapter 配置文档
「跳过去重，把所有抓到的都写进去」 → 拒绝。会污染候选池，下次 recommend 时排序失效
「跳过粗打分，直接写 raw 标题」 → 允许（AUTO_SCORE=false），但提示用户后续需要 /cheat-score 才能进 recommend 池

Integration

上游：用户配置 .cheat-state.json 的 enabled_trend_sources 数组
下游：/cheat-recommend 直接读 candidates.md 排序——trends 写完，recommend 立刻看到
与 /cheat-init：onboarding Q4 选"没有候选池"的用户被引导到这里
与 /cheat-status：status 看板显示"上次抓热点：X 天前 / 待清理候选池：Y 条"

Adapter 实现注意事项

每个 adapters/trend-sources/<name>.md 必须文档化以下：

依赖：API key / cookie / package
fetch 接口：调用方式（python script path / shell command / API endpoint）
输出 schema：必须符合 candidate-schema.md
失败模式：常见错误 + 优雅降级行为
稳定性等级：★ 1-5 颗星

详见 adapters/HOWTO.md（待批次 3 实现）。

Files

Just SKILL.md in skills/cheat-trends of XBuilderLAB/cheat-on-content.

Open the folder on GitHubat commit 2d8211e

Compare with similar skills

Cheat Trends next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cheat Trends compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Cheat Trends this skillXBuilderLAB/cheat-on-content	7.2k	—	~1.4k	Automated safety check: Notes	MIT	2 days ago
Multi Platform Search Searchapigooseworks-ai/goose-skills	1.2k	1 repos	~3.5k	Automated safety check: Pass	MIT	today
Agent ReachPanniantong/Agent-Reach	93k	—	~1.4k	Automated safety check: Pass	MIT	21 days ago
Last30days CnJesseovo/last30days-skill-cn	1.9k	—	~2.4k	Automated safety check: Notes	MIT	today
bb-browser Site Commands for OpenClawepiral/bb-browser	6.2k	—	~1k	Automated safety check: Pass	MIT	4 mo ago
Paid Ads AuditAgriciDaniel/claude-ads	9.8k	—	~1.5k	Automated safety check: Pass	MIT	4 days ago

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Questions about Cheat Trends

What does Cheat Trends do?

When should I use Cheat Trends?

Cheat Trends fits situations like: education work in your project.

How do I install Cheat Trends in Claude Code?

Run `npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a claude-code`. Or copy the skill folder (skills/cheat-trends in XBuilderLAB/cheat-on-content) into .claude/skills/cheat-trends in your project. Claude Code loads it when a task matches its description.

How do I install Cheat Trends in Codex?

Run `npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a codex`. Or copy the skill folder (skills/cheat-trends in XBuilderLAB/cheat-on-content) into .agents/skills/cheat-trends in your project. Codex loads it when a task matches its description.

Can I use Cheat Trends in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add XBuilderLAB/cheat-on-content --skill cheat-trends -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cheat-trends, .gemini/skills/cheat-trends, .github/skills/cheat-trends and .opencode/skills/cheat-trends in your project.

What does Cheat Trends need to run?

SKILL.md names no scripts, command-line tools or credentials: Cheat Trends is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, Glob, WebFetch, Skill.

Does Cheat Trends access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cheat Trends safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Cheat Trends use?

Cheat Trends is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cheat Trends use?

About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cheat Trends?

Skills that share tags, products or a category with Cheat Trends: Multi Platform Search Searchapi (gooseworks-ai/goose-skills, 1.2k stars), Agent Reach (Panniantong/Agent-Reach, 93k stars), Last30days Cn (Jesseovo/last30days-skill-cn, 1.9k stars) and bb-browser Site Commands for OpenClaw (epiral/bb-browser, 6.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cheat Trends?

XBuilderLAB (a GitHub organization) maintains it in XBuilderLAB/cheat-on-content, which has 7,225 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 5, 2026.

Source: XBuilderLAB/cheat-on-content on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Cheat Trends

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

/cheat-trends — 热点抓取

Overview

Constants

Inputs

Workflow

Phase 0: 读启用的 adapters

Phase 1-2: 对每个 adapter 调 fetch + normalize

Phase 3: 去重

Phase 4: 粗打分

Phase 5: 排序 + 询问

Phase 6: 落盘

Phase 7: 状态更新

Key Rules

Refusals

Integration

Adapter 实现注意事项

Files

Compare with similar skills

Similar skills

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Related

Questions about Cheat Trends