Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者による説明(英語の原文)
AI advisory board (MCP): quotes verified word-for-word against public-domain texts, fail-closed
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
リンク先は作者自身のファイルです。私たちが置いたり再パッケージしたりしたものではありません。ファイルを開くと、Claude Desktopがインストールします。
ターミナルでこれを実行します
ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月8日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント ilyautov で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.1.3)がレジストリに届いたのは4 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにMCPBも必要です。
同じカテゴリには:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
English · Русский
Several perspectives. One decision. Quotes checked against the source.
An AI advisory board for Claude Code and MCP hosts. Ask about a difficult choice: advisors examine it through different lenses, challenge one another and give you one next step. A line marked 🔵 is checked by code against the source text word for word. If the line is absent, the advisor does not present it as a quote.
Example: “What would Marcus Aurelius say about this?” Consilium can label a model's interpretation 🟡, show a source-checked passage 🔵, or decline to quote when the corpus does not support one. Watch the live refusal and verification →
Quick start in Claude Code
/plugin marketplace add ilyautov/consilium-principis
/plugin install consilium-principis@consilium-marketplace
Then ask: “Help me decide what to focus on.” For Claude Desktop, other MCP hosts and local setup, see Installation. The plugin uses your existing agent; model/provider charges, if any, follow your host setup.
Explore: See a session · How quote verification works · Installation · Limits
Consilium Principis is a skill and MCP server for AI agents. You connect it to your agent, ask a question you need to decide on, and it gathers a board around the table: Sun Tzu, Marcus Aurelius, Machiavelli, Epictetus, and whoever else you add. Each looks at it through their own lens, leans on their own texts, and argues — with you and with the others. At the end you get one synthesis and one next step. And if the question is quantifiable ("which is more worthwhile, X or Y"), the board doesn't eyeball it — it breaks the decision into numbers and runs them through a Monte Carlo.
This project doesn't ask an AI to roleplay a sage — we tried to build a tool, not a roleplay game or an aphorism generator. Why you can trust its quotes comes next.
One honest thing about who's at the table: these aren't the thinkers themselves but their AI versions — a prism of their views over the corpus of their public texts. Nobody is "speaking from beyond the grave." An advisor holds the author's lens and leans on their words, but it stays a model — and we say so plainly.
Consilium needs no separate API key. It runs on the agent you already have, whose model or provider may have its own charges. Checking quotes needs no internet; a full offline setup requires local models.
The "several AIs deliberate" mechanic is already common. What sets Consilium apart isn't the number of voices. It's that a claim can be re-verified, and where it can't, the council honestly stays silent:
| "Roleplay a sage" (one AI) | LLM-Council-style board | Persona board with a quote check (e.g. roast-room) | Consilium | |
|---|---|---|---|---|
| Different lenses, disagreement as a feature | ± | ✅ | ✅ | ✅ |
| Quote checked word-for-word against the corpus of the advisor it is attributed to | ✗ | ✗ | ± pooled corpus, after the session | ✅ per advisor, before output |
| Honestly silent outside the corpus (fail-closed) | ✗ | ✗ | ✗ | ✅ |
| Verdict computed by code, with caps for ruin risk and irreversible bets | ✗ | ✗ | ✅ | ✅ |
| Quantifiable question → Monte Carlo (📐) | ✗ | ✗ | ✗ | ✅ |
| Different models in the seats, on subscriptions you already have | ✗ | ± API key | ✗ | ✅ Claude Code / Codex / Kimi |
| No separate key required by the project | ± | ± | ✅ | ✅ |
A "personal board of directors" is a mainstream practice (an HBR concept); the bet on grounding and citation transparency has been validated by both the market (Delphi, $16M from Sequoia) and academia (multi-persona debate, the ALCE benchmark). Our angle is rigor: a verifiable quote and an honest refusal.
Live run: an invented Aurelius quote is refused; a real passage is checked against its source. More scenarios — wrong attribution, Monte Carlo, cross-lingual: [demo gallery →](docs/demo/gallery.md)
You ask in plain words:
"I'm juggling four directions at once. What should I focus on?"
And instead of one smooth, hedge-everything answer, you get a session where each advisor looks from their own vantage point, and they disagree:
Sun Tzu (the "Strategist" lens) 🟡 "Whoever is strong everywhere is strong nowhere. Of the four fronts, pick the one where you have the advantage, throw your weight there; hold the rest at a minimum."
Machiavelli 🟡 "Spread thin, you're weak on every front and vulnerable on all of them. The question isn't 'what to grow' but 'what you're willing to give up'. That is the decision."
Marcus Aurelius 🔵 "Every moment think steadily as a Roman and a man to do what thou hast in hand with perfect and simple dignity" *(Meditations, trans. Long)*. Do the thing in front of you wholly; four things at once is scattering.
↳ and ask him something that isn't in his texts, and he'll honestly stay silent instead of inventing a quote.
Next comes the synthesis (where they actually agree) and one concrete next step. Disagreement here is a feature: you see the decision from angles you'd have missed on your own. And if the question is quantifiable ("which is more worthwhile, X or Y"), the board will offer not to argue by eye but to calculate (see 📐 below).
The example is illustrative, and the advisors are AI representations built from texts, not the authors themselves. 🟡 = a thought in the author's spirit (the model reasons through their lens), 🔵 = a verbatim line with a source in the corpus. All examples are public-domain figures (Sun Tzu, Aurelius, Machiavelli, Epictetus); who sits at YOUR table is your call. Your real sessions are written to council/ locally, and never go into git (see technical design).
No need to learn commands. Say "what can you do?" and the board will show a menu. For example:
This isn't "a chatbot playing sage." Every word is tagged by a protective contour.
The real proof isn't in the tone but in verifiability: a verbatim quote (🔵) is checked against the author's genuine corpus by code, word-for-word, and carries a source. If the exact line isn't found, the advisor honestly stays silent (fail-closed) instead of making it up. That's what separates a decision tool from "roleplay a sage": every claim can be re-checked.
| Marker | What it means |
|---|---|
| 🔵 | verbatim quote from the author's genuine corpus, verified word-for-word, with a source |
| 🟢 | verbatim, but from commentary/interpretation (not in the author's own voice), with the commentator named |
| 🟡 | in the spirit of the author, but not their exact words, honestly marked |
| 📐 | calculation: your own numbers, run through Monte Carlo; not truth, not a quote, just your own model run N times |
| *abstention* | question outside the corpus → the advisor stays silent instead of making things up |
Plus different lenses, not a chorus: each advisor keeps their own angle, and their disagreement is a working part, not a bug. You see the decision from sides you'd otherwise collapse into one.
The honesty contour itself is code, not a model: it checks every quote against its source on any machine, no keys, no cost. Without semantics only search precision gets weaker — honesty never does. Candidate relevance is scored by a two-phase gate: a model judges, code applies the threshold, so topically-close-but-not-actually-answering material won't slip through as 🔵. By default the host does that scoring — an interested party, which doctor labels honestly; want an independent judge, connect a local (ollama) or API backend, an explicit choice. The API backend is OpenRouter (openrouter.ai) with your own OPENROUTER_API_KEY: the query and candidate passages are sent there for scoring. Nothing leaves your machine unless you turn it on. The verbatim 🔵 gate never depends on the judge: code holds it.
The board has two modes. The first is a session (discuss, challenge, synthesize). The second kicks in on a quantifiable decision question ("which is more worthwhile", "is it worth it", "X or Y"): instead of an eyeballed answer, the board breaks the choice down into a decision map, that is options, quantities (your own numbers, elicited as three-point "worst / typical / best" estimates, to avoid anchoring), and formulas. Then comes deterministic Monte Carlo (code does the math, zero LLM in the calculation, same seed gives the same result): P(best option), a tornado sensitivity chart, what actually drives the outcome. A "do nothing" option is mandatory. Breaking it down with a premortem ("a year passed, it failed, why?") and a 2×2 on the key uncertainties is also something the board does. Everything sits under the 📐 label, next to 🔵/🟢/🟡 but never blended in: this isn't wisdom, it's your own model, run numerically. And if you log the decision, later the board will check the forecast against what actually happened (the outcome loop: calibrating your model, not "right or wrong guess").
By default, plainly human: the board answers in your language and gives a clean bottom line without extra numbers. A 🔵 quote stays in the original (so it can be verified), with a translation alongside it. Need the full breakdown (probabilities, robustness of the position, source-by-source markup), just say "show me the details."
In Claude Code:
/plugin marketplace add ilyautov/consilium-principis
/plugin install consilium-principis@consilium-marketplace
Claude Code will spin up the MCP server and register the skill. Then say "where do I start", and the concierge will check readiness and assemble a starter board (Marcus Aurelius and Epictetus, public-domain).
From the skills.sh registry, one command installs the thin onboarding funnel:
npx skills add ilyautov/consilium-principis/skills/consilium-connect
This installs only the consilium-connect guide sk
冒頭だけを表示しています。続きはリポジトリで読めます