sith-choir

安装指南

1. ClawHub CLI 从源直接安装技能的最快方式。
   npx clawhub@latest install sith-choir
2. 手动安装 将技能文件夹复制到以下位置之一
   全局模式
   ~/.openclaw/skills/
   工作区
   /skills/
   优先级：工作区 > 本地 > 内置
3. 提示词安装 将此提示词复制到 OpenClaw 即可自动安装。
   请帮我使用 Clawhub 安装 sith-choir。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。
   更倾向于下载？
   获取包含原生技能文件的ZIP压缩包。
   下载技能 (.zip)

什么是

Sith Choir 是 Openclaw Skills 的跨模型智能中继，使一个主导代理能够将每个已配置的 API 密钥作为独立且受健康状态跟踪的语音进行协调。它在多个提供商之间分配工作，使用低成本模型并行生成方案，让更强的推理模型汇总结果，将图像中继给具备视觉能力的模型，并在密钥受到速率限制或不可用时自动进行故障转移。

应用场景

在 Gemini、Mistral、OpenAI-compatible、Groq、Cohere、llm7.io 和其他受支持的提供商之间协调多个 API 密钥。

工作原理

技能加载凭据文件或 arena 工作区，并将每个提供商的 api_key 及每个 alt_api_keys 条目扩展为独立语音。

配置指南

确保 Python 3.9 或更高版本以及技能仓库可用。

数据架构

输入凭据文档包含提供商映射。

高级特性

具有每密钥健康冷却和自动故障转移的多提供商、多密钥网格编排。

name: sith-choir description: "Cross-model intellect relay across a mesh of stored API keys (multi-provider, multi-key). The conductor farms work out to a choir of models reachable via EVERY stored credential — each provider's api_key and each alt_api_keys entry is its own health-tracked voice with automatic per-key failover — and models that need help are answered using other providers' keys (e.g. a Mistral-key model that is rate-limited or unsure consults Gemini and llm7.io keys). Core moves: (1) weak/cheap models sing candidates in parallel and a reasoning model aggregates them (Mixture-of-Agents, arXiv:2406.04692); (2) vision relay — text-only callers hand images to vision-capable voices (arXiv:2502.16428); (3) mandatory escalation — low confidence, errors or rate limits route to a reasoning court from other providers (FrugalGPT cascade, arXiv:2305.05176); (4) judge panels score 0-10 and a persistent reward ledger punishes weak voices while key-health cooldowns rotate load (arXiv:2306.05685). Plus self-consistency voting (consensus, weighted plurality with abstention, arXiv:2203.11171 + 2502.06233), an independent adversarial verify round, a verifier revision loop (refine, Reflexion arXiv:2303.11366), calibration-driven self-improvement (calibrate), multi-turn memory (--session), exact-question answer caching, provider cache-read token accounting + --budget, provenance runs/report with latency, task --lane routing, --plan dry-run, machine-readable --json everywhere, and offline selftest. Use when you hold many API keys and want every one contributing, or when one model cannot do the whole job alone." version: 1.3.0
id: "a9e6f0c2-4b71-4f0e-9c3d-sithchoir0001"
license: MIT
dependencies:

• "python>=3.9" host_compatibility: [Claude, ChatGPT, Gemini, Groq, Qwen, Kimi, Mistral, Llama]