Html5 Game Radar logo

Html5 Game Radar

Community
kennyzir
html5-game-radar

HTML5 游戏发现雷达 - 多源监测又新又热的 HTML5 游戏,识别 SEO 套利窗口。 触发条件: - 用户说「跑 HTML5 雷达」、「启动 HTML5 监控」、「HTML5 游戏扫描」 - 每天 cron 自动触发(建议 10:00 / 16:00) 监测平台: 1. itch.io — 独立游戏最大源头,HTML5 首发首选 2. Reddit r/webgames — 玩家发现新游戏第一线 3. Google Trends — 搜索量爆发判断 4. X/Twitter — 游戏病毒扩散早期信号(基于游戏名搜索) 5. Crazy Games / YouTube — 补充信号源 信号评分(0-20)判断游戏是否值得做 SEO 内容。

Overview

Publisherkennyzir
Repository7deer_skills
Skill namehtml5-game-radar
Stars
329
Forks
143
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kennyzir on GitHub. Read the source before you install it.

Installation

Install the Html5 Game Radar AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kennyzir/7deer_skills/tree/main/html5-game-radar
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kennyzir/7deer_skills.git /tmp/7deer_skills
mkdir -p .claude/skills
cp -r /tmp/7deer_skills/html5-game-radar .claude/skills/html5-game-radar
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Html5 Game Radar in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Html5 Game Radar on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Html5 Game Radar is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HTML5 Game Radar — 游戏发现雷达

核心目标

发现又新又热的 HTML5 游戏,在 SEO 空白期抢先建站截流。

游戏爆发(玩家在找)
    ↓
Google 搜索量 ↑  但内容页 ↓(SEO 空白)
    ↓
你抢先建站 → 截流搜索流量

工作流程

Step 1: itch.io 监测(最新 HTML5 游戏,?sort=date)
Step 2: Reddit r/webgames 新帖扫描
Step 3: Google 搜索量 + 竞争度验证
Step 4: X/Twitter 扩散信号(用游戏名搜索)
Step 5: 汇总评分 + 排序
Step 6: 推送 Feishu

Step 1: itch.io HTML5 新游戏抓取

目标 URL: https://itch.io/games/tag-html5?sort=date

⚠️ 重要: itch.io 的 votes 和 date 是懒加载 JS,无法从 DOM 提取。评分改用「标题关键词 + 类型」作为主要打分依据。

Browser 操作流程:

  1. browser(action="navigate") → https://itch.io/games/tag-html5?sort=date
  2. 滚动 3 次,每次 window.scrollBy(0, 800)
  3. browser evaluate 提取游戏列表

提取字段:

javascript
document.querySelectorAll('.game_cell').forEach(cell => {
  const title = cell.querySelector('.title')?.innerText || '';
  const dev = cell.querySelector('.game_author a')?.innerText || '';
  const genre = cell.querySelector('.game_genre')?.innerText || '';
  const url = cell.querySelector('.title')?.href || '';
  results.push({ title, dev, genre, url });
});

信号判断:

  • 标题含「new」「free」「multiplayer」「io」「survival」= 热门类型信号
  • 游戏类型为 Action/Adventure/Survival/Puzzle = 高价值类型
  • ⚠️ 过滤 Jam 游戏:标题含「jam」「Ludum」「 GMTK」「Game Jam」= 低价值

Step 2: Reddit r/webgames 扫描

scripts/reddit_monitor.py 需要通过 --reddit-script 或专用环境变量 HTML5_REDDIT_SCRIPT 指向已有的 reddit.ts。未配置时停止,不会调用 npx;默认输出 JSON 到 stdout,只有显式 --output 才创建新文件且不会覆盖已有路径。

bash
python3 scripts/reddit_monitor.py --reddit-script /path/to/reddit.ts
python3 scripts/reddit_monitor.py --reddit-script /path/to/reddit.ts --output reddit_output.json

使用 reddit-research skill 的 CLI:

bash
npx tsx reddit.ts new webgames --limit 30

过滤 HTML5/浏览器游戏相关帖子:

bash
npx tsx reddit.ts search "html5 OR browser game OR itch.io" --sub webgames --sort new --limit 20

信号判断:

  • 单帖 upvotes > 50 = 强信号
  • 单帖评论 > 20 = 讨论活跃
  • 含游戏链接(非自荐)= 玩家主动分享

Step 3: Google 搜索量 + 竞争度验证

对 Top 5 游戏名执行:

用 browser 打开 Google 搜索:

https://www.google.com/search?q=[游戏名]+game+online+free

在页面 snapshot 中检查:

  • 搜索结果数量(Accessibility tree 中的「约 X 个结果」)
  • 是否有 Wikipedia / 主流媒体提及
  • 是否有 Fandom wiki / 攻略站

SEO 套利判断:

搜索结果 < 50,000 = ✅ 低竞争(SEO 友好)
搜索结果 50,000-200,000 = ⚠️ 中等竞争
搜索结果 > 200,000 = ❌ 高竞争(红海)
有 Wikipedia = ✅ 流量精准(值得做)
有 Fandom wiki = ✅ 已有内容生态(可截流)

Step 4: X/Twitter 扩散信号

⚠️ 在 Step 1/2 获取游戏名之后执行。用游戏名搜 X,不要泛搜。

搜索词构造:

"[游戏名]" html5
"[游戏名]" "new game" site:twitter.com
"[游戏名]" "everyone playing"

执行流程:

  1. 从 itch.io/Reddit 结果提取 Top 10 游戏名
  2. 对每个游戏构造 1-2 个 X 搜索
  3. 用 browser(action="navigate") 打开 X 搜索
  4. 用 JS 提取:推文数、含游戏链接的帖子

简化收集 JS(X 未登录版最多 9 条):

javascript
document.querySelectorAll('article[data-testid="tweet"]').forEach(t => {
  const text = t.querySelector('[lang]')?.innerText || '';
  const likes = t.querySelector('[data-testid="like"] span')?.innerText || '0';
  if (text.includes('game_name')) results.push({ text, likes });
});

信号判断:

  • ≥3 条相关推文 = 🟢 爆发前兆
  • 1-2 条 = 🟡 关注中
  • 0 条 = ⚪ 沉默期

Step 5: 信号评分(0-20)

⚠️ 注意:itch.io 票数和发布日期为懒加载,无法从 DOM 提取。评分系统改用以下替代信号:

信号维度分值判断依据
标题关键词+1~5含「new」「free」「multiplayer」「io」=+2;含「online」「survival」「roguelike」=+1
游戏类型+1~3Action/Adventure/Survival/Puzzle=+3;Simulation/Strategy=+2;Other=+1
开发活跃度+1~2开发者有多个游戏=+2;首次发布=+1
Reddit 信号+1~4upvotes > 100=4, > 50=3, > 20=2
Google 竞争度+1~4结果<5万=4, <20万=3, <50万=2, >50万=1
X/Twitter 信号+1~3≥3条=3, 2条=2, 1条=1

阈值判断:

  • 15-20 分:🔥 立即行动 — 立刻建站/写内容
  • 10-14 分:✅ 值得做 — 写攻略/合集页
  • 5-9 分:⚠️ 观察 — 加入监控列表
  • < 5 分:❌ 跳过 — 竞争已饱和或信号太弱

Step 6: 推送 Feishu

报告格式:

🎮 HTML5 Game Radar | {{ DATE }}

🏆 Top 信号游戏

## [游戏名]
- 平台:itch.io / Reddit / X
- 类型:[游戏类型]
- 信号分数:{{ }}/20
- SEO 窗口:✅ 低竞争 / ⚠️ 中等 / ❌ 高竞争
- Google 搜索结果:约 X 个
- X 信号:X 条相关推文
- 套利建议:建攻略页 / 合集页 / 评测页
- 链接:[游戏链接]

📊 信号汇总
- itch.io 新游戏:N 个
- Reddit 讨论帖:N 篇(X 条高信号)
- X 扩散信号:N 条
- 高价值候选:N 个(≥10分)

Cron 配置

javascript
{
  "name": "HTML5 Game Radar 每日扫描",
  "schedule": { "kind": "cron", "expr": "0 10,16 * * *", "tz": "Asia/Shanghai" },
  "sessionTarget": "isolated",
  "payload": {
    "kind": "agentTurn",
    "message": "使用 html5-game-radar skill 执行 HTML5 游戏发现扫描:\n\n1. 打开 browser 访问 itch.io ?sort=date,收集游戏名+类型(SKILL.md Step 1)\n2. 用 reddit-research skill 扫描 r/webgames 新帖(SKILL.md Step 2)\n3. 对 Top 5 游戏验证 Google 搜索量和竞争度(SKILL.md Step 3)\n4. 用游戏名在 X/Twitter 搜索扩散信号(SKILL.md Step 4)\n5. 按 0-20 分评分标准打分(SKILL.md Step 5)\n6. 执行完毕后立即用 message 工具发飞书(SKILL.md Step 6)",
    "timeoutSeconds": 600
  },
  "delivery": { "mode": "none" }
}

已知限制

  1. itch.io votes/date 不可提取:懒加载 JS,评分改用标题关键词 + 类型
  2. X 未登录限制:最多 9 条结果,用游戏名精准搜索弥补
  3. Google Trends API:需要 API Key,改用 browser 打开搜索页判断竞争度
  4. Jam 游戏干扰:过滤标题含「jam」「Ludum」「Game Jam」的游戏

参考资料

  • SEO 套利方法论:见 references/seo_arbitrage_logic.md
  • 平台首发特点:见 references/platform_signals.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Html5 Game Radar AI skill do?

HTML5 游戏发现雷达 - 多源监测又新又热的 HTML5 游戏,识别 SEO 套利窗口。 触发条件: - 用户说「跑 HTML5 雷达」、「启动 HTML5 监控」、「HTML5 游戏扫描」 - 每天 cron 自动触发(建议 10:00 / 16:00) 监测平台: 1. itch.io — 独立游戏最大源头,HTML5 首发首选 2. Reddit r/webgames — 玩家发现新游戏第一线 3. Google Trends — 搜索量爆发判断 4. X/Twitter — 游戏病毒扩散早期信号(基于游戏名搜索) 5. Crazy Games / YouTube — 补充信号源 信号评分(0-20)判断游戏是否值得做 SEO 内容。

Why use Html5 Game Radar on TypingMind?

Because you install it once and use it with any model. Html5 Game Radar is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Html5 Game Radar in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kennyzir/7deer_skills/tree/main/html5-game-radar. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Html5 Game Radar?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Html5 Game Radar?

As many as you like. As long as a model supports skills, you can use Html5 Game Radar with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Html5 Game Radar AI skill free?

Yes. It is published on GitHub by kennyzir under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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