booth-query-display

booth-query-display is a skill for Claude Code, Codex from ggg123124/vrchat-assistant. It costs 64 tokens per session (3,341 once invoked), scanned A, original, MIT.

A BOOTH product lookup and display workflow. BOOTH is a Japanese marketplace commonly used for digital goods such as VRChat avatars, clothing, and 3D models.

In plain words
What is it for?
Use it to search products, inspect individual listings, show popularity rankings, retrieve cover images, translate Japanese names, and review saved product or search history.
Why use it?
It avoids repeating searches, reduces requests to BOOTH by using saved results when appropriate, and presents product names, covers, prices in Chinese yuan, and popularity in a fixed format.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to search products, inspect individual listings, show popularity rankings, retrieve cover images, translate Japanese names, and review saved product or search history.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ggg123124/vrchat-assistant/booth-query-display
Install

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.

Any agent
npx skills add ggg123124/vrchat-assistant --skill booth-query-display
Clone the repo
git clone --depth 1 https://github.com/ggg123124/vrchat-assistant

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for booth-query-display

README.md
[![agentmods](https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/booth-query-display/github.svg)](https://agentmods.dev/skills/ggg123124/vrchat-assistant/booth-query-display)
Your own site
<a href="https://agentmods.dev/skills/ggg123124/vrchat-assistant/booth-query-display"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/booth-query-display/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.

agentmods 80×15 button for booth-query-display

Your own site · 80×15
<a href="https://agentmods.dev/skills/ggg123124/vrchat-assistant/booth-query-display"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/booth-query-display.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,341 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.03341
Opus 5 $0.00032 $0.01670
Sonnet 5 $0.00013 $0.00668
Haiku 4.5 $0.00006 $0.00334

Measured 10d ago against content hash 939a2ede542d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

booth-query-display scanned grade A with 1 finding 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 10d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

1. **curl 发中文会乱码**:git-bash 里 curl 传中文 query 会编码损坏(服务端收到 `????`)——用 Python urllib/requests 发 UTF-8 请求
skills/booth-query-display/SKILL.md · 170 lines

How it starts

The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BOOTH 商品查询展示 Skill — 搜索/热度榜/封面/汉化/格式化输出

本 skill 固化「查询 BOOTH(booth.pm)商品并按固定格式展示」的完整工作流。适用场景:用户要求查 Booth 商品、查 VRChat 素材热度榜、展示商品列表(含封面、人民币价格、热度)。

触发条件

  • 「查询 Booth / booth.pm 商品」
  • 「Booth 热度前 N」/「Booth 排行」
  • 「展示 Booth 商品」/「附封面展示」
  • 用户要求查 VRChat 相关素材(avatar/衣装/3D 模型)在 Booth 的售价与热度

BOOTH 缓存交互规则(用户拍板 2026-08-14)

查询 BOOTH 商品时按以下规则决定实时/缓存(不要无脑询问):

  1. 用户明确说要查新的/最新/实时get_booth_itemforceRefresh: true 强制实时抓取
  2. 用户未明确 → 默认走本地缓存get_booth_history / get_booth_searches 查历史;get_booth_item 缓存命中直接返回 cached:true
  3. 仅当拿不准用户意图时才询问一句(如"要最新的还是查过的?")

缓存未命中时代码自动实时兜底(get_booth_item 缓存 miss 后直接抓取并落库),无需文档重复。

落库缓存功能(并入本 skill,Issue #28 实现)

BOOTH 查询结果自动落库(本地 SQLite booth_items / booth_search_history 表,旁路缓存——落库失败不影响实时返回):

工具 说明
search_booth_items 搜索命中即 upsert 商品快照到 booth_items,记录搜索历史
get_booth_item 单品查询命中即落库;缓存命中返回 cached: true(不抓 BOOTH);forceRefresh: true 强制实时
get_booth_history 查已落库商品快照(按收藏数/更新时间排序,minWishlist 趋势过滤)——"上周查过哪件衣服"
get_booth_searches 查搜索历史(搜索词 + 结果 + 时间)
  • 收藏数(wishlistCount)是 BOOTH 唯一公开热度信号,落库后可做趋势跟踪(哪件在涨、接近售罄)
  • 重复搜索同词优先走缓存,避免触发 booth.pm 限流
  • 服务重启数据仍在(SQLite 持久化);老库升级自动建表(IF NOT EXISTS 幂等)

浏览器访问流程(重要修正)

优先使用电脑的默认浏览器,而非临时启动的调试实例:

  1. 检测默认浏览器(Windows):
    reg query "HKCU\Software\Microsoft\Windows\Shell\Associations\UrlAssociations\http\UserChoice" | grep ProgId
    # MSEdgeHTM → Edge;ChromeHTML → Chrome;FirefoxURL → Firefox
    
  2. 用默认浏览器打开目标页(如 Booth 登录页):
    # 默认浏览器直接打开 URL(Windows 用 start / cmd /c start)
    cmd //c start "" "https://booth.pm/users/sign_in"
    # 或显式指定浏览器路径(Edge 示例)
    "/c/Program Files (x86)/Microsoft/Edge/Application/msedge.exe" "https://booth.pm/users/sign_in"
    
  3. 需自动化接管时:给默认浏览器附加 CDP 调试端口启动(必须带独立 --user-data-dir,避免与用户日常浏览会话冲突):
    EDGE="/c/Program Files (x86)/Microsoft/Edge/Application/msedge.exe"
    "$EDGE" --remote-debugging-port=9222 --user-data-dir="$LOCALAPPDATA/Temp/edge-debug-profile" --no-first-run "URL"
    # Chrome 同理;CDP 端点 http://127.0.0.1:9222/json
    
  4. 手动登录页场景(reCAPTCHA 等无法自动化的):
    • 优先用默认浏览器打开页面让用户操作,或
    • 用上述 CDP 实例打开——登录窗口会出现在该实例中,提示用户在对应窗口完成登录(可能与你日常浏览窗口并存,注意区分)

Read the full file on GitHub · 170 lines

Changes

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.

  1. 10d ago First seen · 170 lines · 64 tokens per session scan A 939a2ede542d

Subscribe to this mod's changes

booth-query-display is a skill published in the GitHub repository ggg123124/vrchat-assistant (21 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 3,341 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.