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This walks through a full distillation, from “I want to build an X expert” to an expert ready for review. One thing to know first: distillation is a conversation, but not a casual one. It has numbered phases and hard gates — research can’t be skipped, scenario calibration can’t be skipped, and at least one skill must be produced. When it feels like “why isn’t it writing yet”, it’s usually not stuck; it’s waiting for you to confirm a checkpoint.

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What you get

A DRAFT expert with 12 fields written, at least 1 skill installed, calibrated against 5 scenarios, and ready to submit for review.

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Prerequisites

1

Be an approved creator

Creator onboarding must be approved first. Unapproved accounts are rejected by the backend on submit (the frontend blocks it too).
2

Know your target

Both “distill Munger” and “I want to make better decisions” work, but they take different paths: the first goes straight to clarification, the second spends 1-2 rounds on diagnosis. The more specific your target, the more time you save.
3

Have your materials ready (if any)

Your articles, course notes, case records, existing agent config files all work. Have them ready before starting — the moment you say “I have materials”, the distiller stops and waits for the upload, and won’t proceed without it.

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Steps

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Step 1: Open a distillation conversation

Create a new expert in Studio and choose distillation. A DRAFT expert is created immediately (identifier shaped like distilled-a1b2c3d4, version 0.0.1), and all writes land on it. Closing the tab mid-way loses nothing — the draft is in your works list. Open with a clear target:
That last clause matters. Say you have materials and it will wait, rather than running public research first and bringing you a framework you don’t endorse.

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Step 2: Upload materials

Send your stuff in. Documents, PDFs, Markdown, exported conversation logs all work.
Then say explicitly “materials are complete, start research.” It needs a clear go signal.

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Step 3: Wait for six-dimension research, then actually read the summary

Research covers six dimensions: works, dialogues, expressions, third-party views, major decisions, and timeline. With your materials present, they lead and public sources supplement. When it finishes, it stops and hands you a quality summary, waiting for your OK.
This is the cheapest correction point in the pipeline. Let it pass and everything written afterwards rests on the wrong direction — reworking means starting over.Read line by line for two things: are the core arguments it extracted actually yours, and are the sources it cites ones you’d stand behind.
If it’s wrong, say so:

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Step 4: Watch it write the fields

After you confirm, it synthesizes and writes. You’re mostly a spectator here, but it helps to know where things land: Mental models are capped at 3-7 and decision heuristics at 5-10. More isn’t better — past 7 mental models means no choices were made, and the expert comes across as having no position in real conversations.

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Step 5: Skill generation

Every distillation produces at least one skill. A skill is a reusable procedure, not your opinions — those are already in persona. For the pricing advisor, a sensible skill is a “pricing plan evaluation” procedure: given product shape, user scale, and competitor pricing, output tier recommendations and risk flags.
Skill names must be English kebab-case (e.g. pricing-tier-evaluator). Non-ASCII names make the skill permanently un-activatable — that’s a hard constraint, not a style preference.

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Step 6: Scenario calibration

It will stop and ask whether to begin calibration. Reply “continue”, and it sends 5 test scenarios at once:
  1. A known question inside your expertise — is the answer right
  2. A tradeoff question — does it choose like you
  3. A voice test — does it sound like you
  4. A boundary question — does it know what it doesn’t know
  5. An out-of-scope question — does it force an answer anyway
Every question has clickable options, and you can always write your own.
Questions 4 and 5 deserve the most care. Buyer trust collapses almost exclusively at these two points: an expert that answers everything stops being believed after the second try.If it produces a plausible-sounding answer to the out-of-scope question, correct it explicitly: “you should say you don’t know here and suggest talking to an accountant.” That sentence goes into soul.

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Step 7: Verification and submission

It checks field fill state and character counts, mental model count, and whether honesty boundaries are explicit, then tells you to publish from Studio.
Publishing does not happen in the conversation. The distiller has no publish permission. Its closing “click Publish in the top right” is the end of the pipeline, not a brush-off.
Three things worth doing yourself before submitting:
  • Chat with it in Studio for five minutes, asking questions your real customers would ask
  • Check that the persona content is all still there — every write is a full-field overwrite, so if it was revised repeatedly mid-conversation, give it a scan
  • If MCP connections were configured, replace the placeholder API keys with real credentials

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Common sticking points

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One test for quality

Whether a distillation went well isn’t measured by how full the fields are. It’s this: put it next to a general-purpose model answering the same domain question — can you tell them apart at a glance? If you can’t, the distillation produced generic platitudes, usually because the conversation got abstract descriptions instead of real cases. Going back with 3-5 concrete cases and running it again beats rewording the fields by a wide margin.

Expert distillation (mechanics)

The 12 fields, phase gates, and skill naming rules

Configure your expert

The four prompt layers and every config field

Become a creator

Onboarding requirements and levels

Pricing

How unlock fees relate to conversation costs