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 ggg123124/vrchat-assistant --skill emoji-notesgit clone --depth 1 https://github.com/ggg123124/vrchat-assistantWrote 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/ggg123124/vrchat-assistant/emoji-notes)<a href="https://agentmods.dev/skills/ggg123124/vrchat-assistant/emoji-notes"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/emoji-notes/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/ggg123124/vrchat-assistant/emoji-notes"><img src="https://agentmods.dev/badge/skills/ggg123124/vrchat-assistant/emoji-notes.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 | $0.00000 | $0.01094 |
| Opus 5 | $0.00000 | $0.00547 |
| Sonnet 5 | $0.00000 | $0.00219 |
| Haiku 4.5 | $0.00000 | $0.00109 |
Grade A, and why
emoji-notes 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 5d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
emoji-notes 插件(Skill 文档)
用途
给自定义 / 内置 boop emoji 存个性化备注/别名(本地 sqlite 个人数据),并把可能带中文 STT 噪声的口语描述(如「狐狸检查PR」「弧狸检查PR」「审核PR那个」「大笑」)鲁棒反查成 emojiId,再交给 send_boop 执行。解决「每次都要追问用户是哪个 fileId」的痛点。
提供三个 MCP 工具:set_emoji_note(写·本地)/ get_emoji_notes(查)/ resolve_emoji(检索)。
工具用法
1. 给表情设备注/别名(一次成本,之后语音检索永久生效)
set_emoji_note { emojiId: "file_xxx", note: "狐狸在检查 PR 的图",
aliases: ["狐狸检查PR", "审核PR", "狐狸"], tags: ["审核"], category: "梗图" }
- 内置表情
emojiId形如default_laugh(见get_boop_emojis);自定义表情用upload_emoji返回的 fileId。 - 别名是「整表替换」语义;
note与aliases都为空时软删除该条。 - 防御上限:note ≤ 2000 字符、aliases ≤ 50 个、单别名 ≤ 100 字符。
2. 查询备注
get_emoji_notes {} # 全部有效备注(默认 100 条)
get_emoji_notes { emojiId: "file_xxx" } # 精确查单条
get_emoji_notes { kind: "custom" } # 只查自定义
get_emoji_notes { includeDeleted: true } # 含软删除项
3. 口语描述反查 emojiId(resolve → send_boop 链路)
resolve_emoji { query: "狐狸检查PR" }
# matched=true → { emojiId: "file_xxx", confidence: 1.0, matchedBy: "alias", ... }
# 然后:
send_boop { userId: "usr_xxx", emojiId: "file_xxx" }
resolve_emoji { query: "大笑" } # 无备注也能命中内置 default_laugh(builtin_zh)
resolve_emoji { query: "laugh" } # 内置英文名(builtin_en)
resolve_emoji { query: "弧狸检查PR" } # STT 同音错字 → 拼音同音命中(pinyin, 0.95)
匹配信号(matchedBy):alias / pinyin / token_overlap / fuzzy_pinyin / fuzzy_hanzi / builtin_zh / builtin_en。
纪律:先 resolve 拿 emojiId,歧义必须反问用户
- 写路径不猜:
send_boop只收emojiId;任何「用户口述描述」都要先走resolve_emoji。 - 歧义必须反问:
resolve_emoji返回needsClarification=true(或matched=false)时,绝不擅自选用第一名候选,必须把candidates(降序,带confidence和matchedBy)展示给用户确认:「你是说『狐狸检查PR』那张,还是『狐狸吃鸡』那张?」 - 未备注的自定义表情无法靠文字命中:resolve 无候选时,引导用户
set_emoji_note补一句备注/别名(一次性),不要反复问 fileId。 - 判定规则(
resolve.js顶部常量):top.confidence ≥ 0.85且top - second ≥ 0.2才唯一命中;否则返回候选。
依赖
- 唯一运行时依赖
pinyin-pro(纯 JS 拼音库,无 native 编译)。插件自带package.json声明依赖。 - 若启动报「缺少依赖 pinyin-pro」,执行:
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 65 lines · 0 tokens per session scan A 6819d6412281
emoji-notes is a skill published in the GitHub repository ggg123124/vrchat-assistant (21 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,094 tokens. 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 2026-09-06.
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