Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
제작자가 쓴 설명(영어 원문)
AI advisory board (MCP): quotes verified word-for-word against public-domain texts, fail-closed
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
링크는 제작자의 파일로 바로 연결돼요. 우리가 보관하거나 다시 패키징하지 않아요. 파일을 열면 Claude Desktop이 설치해요.
터미널에서 이 명령어를 실행하세요
브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 ilyautov 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.3)은 4개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 MCPB도 있어야 해요.
같은 카테고리에는: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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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
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