Amicus
概览
作者提供的说明(英文原文)
Multi-model LLM Council and parallel AI window for Claude Code. Fork any model, fold results back.
PopNx MCP 的三大支柱
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
- 用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
- 我们的基础检查i1 项中通过 1 项依据公开信息检查;尚未扫描代码
- 使用情况i起步阶段↓ 375/周 · ★ –
它能帮你做什么
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
- 连接方式: 在你的电脑上运行
- 传输方式: stdio
- 分类: 搜索与 AI 记忆
- 你需要填写:OPENROUTER_API_KEY
安装
选择你使用的 AI 应用,然后复制设置。
安装之前
- 基础检查:1 项中通过 1 项
- 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
- 需要你自己填写:
OPENROUTER_API_KEY,AMICUS_SKIP_POSTINSTALL
标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
- npm已安装 Node.js(用于 npx 命令)
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"amicus": {
"command": "npx",
"args": [
"-y",
"amicus",
"mcp"
],
"env": {
"OPENROUTER_API_KEY": "your-value",
"AMICUS_SKIP_POSTINSTALL": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add amicus --env OPENROUTER_API_KEY=your-value --env AMICUS_SKIP_POSTINSTALL=your-value -- npx -y amicus mcp运行前请先填写空着的值。
或者自己复制设置
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"amicus": {
"command": "npx",
"args": [
"-y",
"amicus",
"mcp"
],
"env": {
"OPENROUTER_API_KEY": "your-value",
"AMICUS_SKIP_POSTINSTALL": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
或者自己复制设置
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"amicus": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"amicus",
"mcp"
],
"env": {
"OPENROUTER_API_KEY": "your-value",
"AMICUS_SKIP_POSTINSTALL": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
需要你填写的值
- OpenRouter API key (recommended multi-model route). Optional when a provider key is configured via 'amicus setup' (~/.config/amicus/.env) or OpenCode auth.json.
AMICUS_SKIP_POSTINSTALLenvSet to 1 when a plugin/client manages registration itself (skips npm postinstall side effects).
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它能做什么
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月9日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 BourbonDog 验证,该账户拥有此仓库
- … 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
- … 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
- – 不适用
端点有响应
它在你的电脑上运行,没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行,改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与“搜索与 AI 记忆”中的其他服务器比较i
- 星标数多于此分类中其他 5756 个服务器的 0%
- 发布到注册中心的时间比此分类中其他 5756 个服务器的 60% 更新
- 基础检查结果等于或优于此分类中其他 5756 个服务器的 100%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
常见问题
Amicus 安全吗?
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
它需要哪些密钥或代码?
你需要填写:OPENROUTER_API_KEY
它在我的电脑上运行还是在云端?
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
它还在维护吗?
GitHub 仓库最后更新于–,最新版本(4.14.3)于25天前进入注册中心。
哪些应用可以使用它?
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
有什么可以替代它?
同一分类中有:Context7, Codebase Memory, Exa
替代选择
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Amicus
A multi-model LLM Council for Claude — with a parallel AI window underneath.
Hand Claude a plan, a design, a diff, an architecture decision, a manuscript — anything — and say *council review this*: Amicus routes it through several models from different families, has them anonymously cross-review each other, and a non-Claude chair synthesizes a verdict you turn into accept/deny edits. Or skip the ceremony and fork a single conversation to Gemini, GPT, DeepSeek, or any other model — it works in parallel with full context, and you fold the result back when you're ready. Claude orchestrates throughout; you stay in your editor.
The same council — more ways to run it: take it headless in CI with no Claude runtime, sharpen it with a debate round, or run it on free local models (Ollama, LM Studio, vLLM) at $0 — private and offline.
[Quick start ↓](#quick-start) · Commands · Documentation · Troubleshooting
Supported clients: Claude Code CLI, Claude Desktop, and Claude Cowork are fully tested and supported. Claude Code web is experimental.
Table of Contents
- What is Amicus
- The Council
- Ways to run the council
- Quick start
- 1. Install
- 2. Configure — don't skip this
- 3. Your first council
- 4. Your first sidecar
- Requirements & Dependencies
- The parallel window
- Commands
- Models
- MCP integration
- Configuration
- JSON output
- Windows
- Troubleshooting
- Documentation
- Contributing
- Built on OpenCode
- Attribution & License
What is Amicus
One install delivers six things that work together:
- The `second-opinion` LLM Council skill. Structured multi-model review: independent reviews → anonymized peer cross-review → a non-Claude chair verdict → tiered accept/deny decisions. This is the hero.
- The `sidecar` chat skill. Ad-hoc fork/work/fold — spin up one other model in a real window (or headless), work alongside it, fold the summary back.
- The `amicus` CLI (with an `am` alias) and an MCP server. The engine underneath both skills: launches sessions, shares context, runs parallel waves, and exposes the same surface to Claude as MCP tools.
- A self-updating model catalog. Aliases and validation resolve against a live catalog fetched from provider APIs (cached locally), so model names stay current without a hard-coded table.
- Observability.
amicus watchrenders any live or finished run (fan-out or council) from any terminal;--followstreams milestones as they happen;--on-completefires a hook when a run lands;--retry-failedplus opt-in cheaper-model fallbacks recover dead legs without relaunching the whole wave;amicus spendanswers "what did this cost, and where" with per-run attribution. - Council Workspace.
amicus watch --ui: a window that shows a council *thinking* — live seats, the anonymized judge packet, the adjudication matrix, dissent drill-in, chair verdict, and cost-by-seat — for both live and historical runs. It also auto-opens on an MCP-invoked council run from Claude Code (local), so you no longer have to remember the flag (see docs/council.md).
Claude is the orchestrator. The council and chat skills run *on top of* the engine; you talk to Claude, and Claude drives Amicus.
The Council
Trigger it by saying *"council review this"* to Claude, or, on the plugin channel, run `/amicus:council` directly.
Why multi-model. Any single model — including the one running your session — has consistent blind spots. Route the *same* material through models from *different* families and the disagreements surface: missed issues, overstated confidence, claims one model alone would have waved through. The council is the structured version of that idea.
The flow, in five beats:
- Independent reviews. Each council model reviews the artifact on its own (one parallel wave), producing a structured findings list — claim, severity (
blocker | major | minor | nit), location, rationale. - Anonymized cross-review. Claude relabels every review (Review A, B, C…) and sends the identical bundle to every model. Each model ranks the reviews and adjudicates every finding (
agree | dispute | neutral) — *unknowingly judging its own*, so self-bias washes out. This yields a street-cred ranking and sorts findings into Disputed / Confirmed / Contested / Singleton tiers. Self-bias is washed out per seat: if you deliberately seat one model twice, each seat's vote on the *other* seat's finding is counted as the real peer vote it is, and the finding is flagged when its only corroboration came from its own twin. - Chair verdict. A designated non-Claude chair receives the de-anonymized picture — all reviews, rankings, and adjudications — and synthesizes an independent verdict. Claude presents it verbatim; Claude does not synthesize.
- Tiered decisions. Confirmed findings get one bulk accept/deny; Contested and Singleton findings are decided one at a time (accept / deny / modify).
- Outputs applied. Accepted findings are written into a reviewed copy of the source; the full run is captured in the run folder.
flowchart LR
A["Artifact"] --> B["Independent reviews"] B --> C["Anonymized cross-review"] C --> D["Chair verdict (non-Claude)"] D --> E["Tiered accept / deny"] E --> F["Reviewed copy
- run folder"]
What a run produces (in output/ -council/):
review- .md× N — each model's independent review.crossreview-matrix.md— the adjudication grid plus the de-anonymized street-cred table.verdict.md— the chair's synthesis.report.md— synthesis + the full decision log + a per-call run-stats table.report.html— the deterministic renderer output (adjudication matrix, street-cred table, findings-by-tier, cost — no chair prose). This is the default artifact handed to the user.- For an editable source, the accepted edits land in
-reviewed.next to the original.
Optional council elements (v2.2.0, all default off): four opt-in behaviors, offered once as a menu at launch — nothing turns on unless you name it, and the confirmation lists exactly what's on. Chair verdict scale *(standard since v2.2.0's follow-ups — no longer opt-in)*: the chair always closes with 3–5 hard questions and one parseable VERDICT: Ship it | Fix these first | Fundamental rethink line.
- Critic seat — one reviewer swaps to a four-pass adversarial brief (adversarial pass, edge-case hunt, consistency check, executability test). Its findings enter the same anonymized bundle as everyone else's, so the bench disciplines the critic: manufactured negativity lands Disputed and dies in the tally.
- Expert lenses — each reviewer takes a distinct expert perspective; you pick the panel domain (business, technical, customer, financial, or custom). Lens runs never feed the reliability ledger, and the report discloses the weakened cross-review anonymity.
- Debate mode — after cross-review, every Contested or Disputed finding goes back to its raiser to defend, amend, or withdraw, and the disputing judges re-vote. Exactly one rebuttal round, then the final tally.
- Claude in the council — Claude adds its own fresh review to the bundle so the bench ranks and adjudicates it. Claude is *judged* but never votes or chairs, so the verdict stays independent.
The critic and lens methodologies are adapted from the /critic and /debate agents in John Renaldi's product-kit (MIT); the briefing boilerplate lives in skills/second-opinion/SEAT-BRIEFS.md.
Cost is disclosed up front. Before any model launches, you see the run shape — including any enabled optional elements — for example:
This run uses 3 council models across 2 fanout waves + 1 chair call, with critic seat + debate mode ON (~7 base runs + up to 6 rebuttal calls).
Then the council waits for your confirmation.
The skill lives at [`skills/second-opinion/SKILL.md`](./skills/second-opinion/SKILL.md); the design spec behind it is [`skills/second-opinion/COUNCIL-DESIGN.md`](./skills/second-opinion/COUNCIL-DESIGN.md). For what amicus council tally|verdict|report|stats actually take as input and produce — field-by-field schemas, verdict.json's provenance, and a full worked example run against the real CLI — see [docs/council.md](./docs/council.md).
Ways to run the council
The council is the hero — start with the everyday way, and reach for the more powerful ways when you need them:
- Just ask, in Claude Code. Hand Claude a plan, diff, design, or manuscript and say *"council review this."* The
second-opinionskill runs the whole ritual above in your session, with no setup beyond your API keys. This is how most people use it. → Quick start - Headless, in CI, with no Claude runtime.
amicus council run --prompt-file plan.md --council freeruns that same pipeline in one command — reviews → cross-review → tally → chair verdict — writingverdict.jsonandreport.html. It needs no Claude session, so it drops straight into CI. → Headless council (CI) - With a debate round. Add
--debateand every Contested or Disputed finding goes back to its raiser to defend, amend, or withdraw while the disputing judges re-vote — exactly one rebuttal round, then the final tally. → The Council - On free, local, private models — at $0. Point the council (and sidecars) at an OpenAI-compatible server already running on your machine — Ollama, LM Studio, or vLLM — with
amicus provider add. No API key, no per-token bill, nothing leaves your machine, and it works
只显示了开头部分;其余内容请在仓库中阅读