rikkahub-sillytavern-android: 安卓AI聊天端:开箱即聊,无损导入酒馆卡,缓存强省用量,Jev 决策,专属RP模式,防空回,主动信息,前端卡支持

by MiaoWuNYA · LLM Plugin · ★ 128

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 59/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-chatai-clientandroidcharacter-cardclaudegeminijetpack-composekotlinllmlorebook

Quick Facts

Stars128
Forks11
LanguageKotlin
CategoryLLM Plugin
LicenseAGPL-3.0
Quality Score59/100
Open Issues15
Last Updated2026-10-22
Created2026-09-20
Platformsclaude-code, cli, gemini
Est. Tokens~20k

About rikkahub-sillytavern-android

RikkaHub Plus · 华灯版 安卓上的 AI 聊天客户端,兼为 SillyTavern(酒馆)兼容端 开箱即聊 · 提示词缓存省 token · 记忆不失忆 · 角色卡 / 世界书 / 预设按酒馆官方语义一键导入 简体中文 差异手册 RikkaHub 的深度定制分支(已合入上游 v2.5.2,另有 2150+ 提交的增量),Kotlin + Jetpack Compose 原生实现。 - 只想找个好用的 AI 聊天 App:接上任意 API 就能聊——多供应商、流式输出、语音通话、插件工具、记忆系统、缓存省钱,装上就用,不需要懂任何"酒馆"概念 - 从桌面端酒馆 migrating:角色卡、世界书、预设、正则、美化主题按官方语义无损导入,无需 Termux 或 Node.js 两条路线共用同一个客户端,互不干扰。 ✨ 相对上游多了什么 Gemini 走 OpenAI...

Compatible Skills

These tools work well together with rikkahub-sillytavern-android for enhanced workflows:

  • claude-android-ninja — semantic(0.41)+complementary+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • AiCode — semantic(0.39)+complementary+rare_topics+same_lang+similar_pop+shared_platform (68%)
  • compose-ai-tools — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • Deskdrop — semantic(0.20)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (65%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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