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This walks the full distillation: give it a person or a topic, end up with an expert draft that has every core field filled, at least one skill, and passed scenario calibration — ready to enter the publish flow. First, correct the most common misconception: distillation is not copying what someone said. Copying phrasing gives you a bot that recites quotes and falls apart the moment you ask something it hasn’t memorized. What distillation extracts is a thinking framework — which mental models they view problems through, which heuristics they judge by, what they categorically won’t do, and an honest account of what they can’t do. The former is WHAT they said; the latter is HOW they think. Only the latter generalizes to questions it has never seen.

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Prerequisites

1

Approved creator status

The distillation studio is creator-only. Without approval you’re redirected back to Studio. See Become a creator.
2

Know who it's for and what it's for

“Distill Munger” and “build a Munger-perspective advisor for reviewing my investment decisions” produce very different quality. The latter gives distillation a basis for making trade-offs.
3

Gather materials

Have primary sources ready if you can (manuscripts, transcripts, your own long-form writing). Material quality sets the ceiling — public search alone can produce a usable expert, but rarely a distinctive one.

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The key mechanic: distillation writes these 12 fields

Every “write” in a distillation conversation lands content in one of the target expert’s fields. There are exactly 12 writable ones: Skills aren’t among the 12 — they’re separate objects created with a different tool and can carry scripts and reference material.

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Six phases

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Phase 0: Entry routing

Open the studio and state your intent. Two paths follow: Either path asks separately whether you have materials. Answer carefully:
  • You have them → say so, send everything, then say “that’s all.” It will stop and wait rather than searching first.
  • Archives → sending a zip is fine; it unpacks before reading. Do not expect it to read inside a zip without unpacking.
  • You don’t → say so and it proceeds to search.

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Phase 1: Multi-source collection

Six dimensions: written work, conversations (podcasts/interviews), expression (social media), outside views (analysis and criticism), decisions (major calls and turning points), and timeline. Three hard requirements:
  1. Note source credibility — primary beats secondary beats inference
  2. Distinguish “what they said,” “what others said about them,” and “what I inferred”
  3. Keep contradictions when you find them — don’t split the difference
Sources that are mostly second-hand commentary are excluded, because someone else’s interpretation gets mixed in and read back as the subject’s own view. Collection ends with a research-quality summary and a pause. This is the first checkpoint. The right move here isn’t “continue” — it’s checking two things: is the primary-source share adequate (target >50%), and is any dimension essentially empty. Waving this through means everything synthesized afterwards rests on nothing.

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Phase 2: Framework synthesis

The core of distillation. Output is written into fields incrementally:
Filtered down from 15-30 candidate claims against three tests: cross-domain recurrence (they apply it in unrelated fields), generative power (it produces new conclusions rather than describing), and exclusivity (others don’t think this way).Each model must carry: name, one-line description, ≥2 source situations, how it’s applied, and its limits. That last one is the most-skipped and the most important — it’s what separates a thinking model from a universal platitude. A model with no stated failure conditions is usually a truism.
Shaped as “if X, then Y,” each with a real case. These are the intuitive rules the expert applies to novel problems.
At least 2 pairs of internal contradictions (two things they believe that pull against each other) and at least 3 concrete limits (explicitly saying “I’m weak here” or “I may be biased here”).Honest boundaries aren’t a disclaimer — they’re what makes the expert say “I don’t know” instead of fabricating when it’s out of its depth. Experts missing this fail most visibly on edge questions.
Frequent words, sentence shapes, analogies, humor. The acceptance test is concrete: 100 words should be enough to recognize who it is.
Things they categorically don’t do. Negative constraints lock in voice more effectively than positive description.

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Phase 2.7: Configuration assembly and skill generation

After the framework, tools are configured and at least one skill is generated. Skills are what let the expert actually do work (a deep-research procedure, a decision-review template) rather than only talk. Two hard rules:
  • Skill names must be English kebab-case (decision-analysis, data-pipeline). Non-English names make the skill uncallable, and saving creates duplicates instead of overwriting.
  • Don’t turn the cognitive layer into skills. Mental models, decision heuristics, expression DNA, anti-patterns, and honest boundaries are content fields. Making them skills means they only apply when activated, instead of always.
If a skill carries scripts or reference material (scripts/, references/ directories), it must be saved as a whole directory — otherwise only the skill description text is stored and the scripts are lost.

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Phase 3: Scenario calibration (mandatory)

Once core fields are written and at least one skill exists, 5 test scenarios are generated for you to confirm, covering five dimensions: Each scenario offers three options: matches / not quite / skip. When you pick “not quite,” say exactly what’s wrong — it uses that to revise the corresponding field. Clicking “matches” through all five wastes the step, and the fifth scenario deserves the most attention: an expert unwilling to say “I don’t know” will produce confident, professional-looking wrong answers forever after launch.

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Phase 4: Quality verification

An automatic pass against the checklist: Failing sends it back to Phase 2 with the weak areas flagged, for at most two loops. Failing twice usually means the material is thin — supply primary sources rather than letting it keep searching.

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Phase 5: Finish and publish

Publishing does not happen in the conversation. When distillation finishes it tells you to click the Publish button in the top right, which takes you to the expert editing page. There you review the content, fill in pricing and other details, and go through the formal publish review.Saying “publish it for me” in the conversation has no effect.

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What a distilled expert looks like

A draft expert is created automatically during distillation, with: The draft is always available from Studio, and distillation conversations can be interrupted and resumed — re-entering checks the current field status and continues from where it stopped rather than starting over.

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Updating an existing expert

Say “there’s news about X, update it” for an already-distilled expert and it will:
  1. Read the existing content first
  2. Research only what’s new
  3. Update the relevant fields incrementally, without rewriting the whole profile
That’s deliberate — a full rewrite would wipe out the calibration you already did.

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Migrating from other platforms

Upload project files from another AI platform (recognizable by markers like CLAUDE.md, .cursor/rules/, openclaw.json) and content is routed by meaning: Migrated content still needs scenario calibration. A prompt tuned on another platform won’t necessarily behave the same under this runtime.

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Boundaries and failure modes

Distillation

Distillation overview

Expert configuration

Four-layer prompt injection order

Pricing and billing

What to set before publishing

Self-evolution

How the expert keeps improving after launch