Consilium Principis

A personal board of AI advisors grounded in public-domain texts; every quote is verified word-for-word against its source by code, or the advisor abstains (fail-closed). Runs locally as a stdio MCP server.

A personal board of several AI advisors — for decisions where one confident answer isn't enough.

Live: a made-up "Aurelius quote" is refused; a real line is confirmed 🔵 word-for-word with its source. The verdict is computed by code on every run, not scripted.

Each advisor looks through their own lens, argues with you and with the others, and the board converges on a single next step. But above all: Consilium doesn't pass off a plausible fabrication as the author's words — a 🔵 quote is checked by code against the source textword-for-word; with no exact line in the corpus, the advisor simply doesn't quote.

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 theirAI 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.

No extra keys or payment — Consilium runs on the agent you already have. Checking quotes needs no internet; a full offline setup just takes 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 bere-verified, and where it can't, the council honestly stays silent:

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.

The refusal + word-for-word verification is in the demo up top. More scenarios — wrong-mouth attribution, Monte Carlo, cross-lingual:demo gallery →

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 tocouncil/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:

- "What could go wrong with my launch?"a premortem: where it'll break, what to shore up in advance
- "What would Aurelius say about burnout?"a single advisor, strictly from his texts
- "Have Sun Tzu and Machiavelli argue about X"pit two viewpoints against each other
- "Which is more worthwhile, going back to a salaried job or sticking with my own thing?"📐 a decision map: break it into numbers and calculate (rather than argue by eye)
- "Help me win this argument"a position breakdown like a chess engine: your moves, counters, an honest verdict
- "Am I contradicting myself?"a mirror: what you say vs. what you actually choose

This isn't "a chatbot playing sage." Every word is tagged by aprotective 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.

Plusdifferent 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, whichdoctorlabels honestly; want an independent judge, connect a local (ollama) or API backend, an explicit choice. The verbatim 🔵 gate never depends on the judge: code holds it.

Calculate, not just discuss: the 📐 decision map

The board has two modes. The first is asession(discuss, challenge, synthesize). The second kicks in on aquantifiabledecision question ("which is more worthwhile", "is it worth it", "X or Y"): instead of an eyeballed answer, the board breaks the choice down into adecision map, that is options, quantities (your own numbers, elicited as three-point "worst / typical / best" estimates, to avoid anchoring), and formulas. Then comesdeterministic 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 theforecast against what actually happened(the outcome loop: calibrating your model, not "right or wrong guess").

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