Home / yan-labs / yan-skills · game-opportunity/SKILL.md · GitHub

game-opportunity skillA

game-opportunity is agent-read markdown (skill) from yan-labs/yan-skills: Rankup 的小游戏机会专项 Skill。用于监测游戏平台 sitemap、发现 24 小时游戏新词、建立多语言关键词需求簇、核对 KD/SERP/近 7 天趋势/独立需求/可玩供给并生成每日决策报告;通用规则和脚本随 Rankup 提交,项目私有结果写进被 Git 忽略的 .rankup/。.

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# Game Opportunity

把游戏平台的新 URL 变成可直接挑选的建站候选。商业判断看今天的搜索需求、竞争盘面和可玩供给;
游戏发布时间只负责标注新旧,老游戏同样可以进入优先队列。

<intent-routing>

## 从一句话反查入口

用户不会说「跑 evaluate」,他会说「看看今天有什么游戏能做」。所有命令都是
`node game-opportunity/scripts/game-opportunity.mjs <子命令>`。

| 用户可能说的话 | 跑什么 → 判读依据在哪 |
|---|---|
| 「跑一下小游戏监测」「今天有什么游戏能做」 | `daily`(= `collect` + `demand` + `evaluate`)→ 日报 `.rankup/demand/game-review/latest.md`,分组判读看下方「外部需求双轨闸门」 |
| 「只看今天平台新增了什么」 | `discover`(平台 sitemap diff);只要 24 小时新名字用 `radar` |
| 「查一下这批游戏的量和 KD」 | `plan` → `demand`(**每天只有 6 个深查名额**,名单由你写进 `YYYY-MM-DD-demand-selection.json`,见「深查名额」) |
| 「这些候选值不值得做」 | `evaluate` 排版事实 → **判读归你**:对照「搜索需求轨 / 早期爆发轨」分值表与 KD × 新站动作表,把结论写进 `YYYY-MM-DD-evaluation.json` |
| 「今天这轮算跑完了吗」 | `collect-checklist` / `decision-checklist`(各 10 项证据验收,查的是证据齐不齐,不是判决对不对) |
| 「不联网重建一下今天的清单」 | `dedupe`(读当天 discovery/radar,去重游戏与社交 campaign) |
| 「游戏站怎么搭、怎么变现」 | 不在本 Skill:读 `rankup/references/game-sites.md` + `rankup/references/lifecycle.md` |
| 「选词/外链/建站的通用问题」 | 回 `rankup`——本 Skill 只管「哪个游戏值得做」这一段 |

**任何来源采集失败或某行 `status:'not-queried'` 时,对应项标「未测」,不得当成 0 或「无需求」。**

</intent-routing>

## 分工铁律:脚本只采集,判断只归 AI

| 层 | 谁做 | 产出 |
|---|---|---|
…

Read the whole file at its exact version.

How to install

Latest version
mdr add yan-labs/yan-skills/game-opportunity@git:20260921.28c4148
Exact content
mdr add yan-labs/yan-skills/game-opportunity@sha256:b4df8223e4e4b7e2

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_yxmxdhf46ln5bs42.svg)](https://markdownregistry.com/a/art_yxmxdhf46ln5bs42)

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Versions

versioncommittedcommitsizeaudit
git:20260921.28c4148 latest2026-09-21 28c4148 29,143 BA view · diff
git:20260912.cc47c932026-09-12 cc47c93 27,225 BA view · diff
git:20260830.f8ac2dd2026-08-30 f8ac2dd 26,641 BA view · diff
git:20260830.b5175582026-08-30 b517558 24,913 BA view · diff
git:20260827.f139d152026-08-27 f139d15 21,341 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (29143 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

yan-labs/yan-skills · 203 stars · license MIT · pushed 2026-09-23 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_yxmxdhf46ln5bs42
GET https://markdownregistry.com/api/v1/resolve?ref=yan-labs/yan-skills/game-opportunity
GET https://markdownregistry.com/api/v1/blob/b4df8223e4e4b7e2e20702ac727875ccdec1cd3343795c15bc50672dc73e866f

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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