juxt/allium/distill

Distillation guide

This guide covers extracting Allium specifications from existing codebases. The core challenge is the same as forward elicitation: finding the right level of abstraction. In elicitation you filter out implementation ideas as they arise. In distillation you filter out implementation details that already exist. Both require the same judgement about what matters at the domain level.

Code tells you how something works. A specification captures what it does and why it matters. The skill is asking "why does the stakeholder care about this?" and "could this be different while still being the same system?"

Interaction modes

This skill runs in two modes. Every instruction below that asks, prompts or validates with the user follows the mode:

• Interactive — running inline in a conversation. Ask the user directly and wait for the answer.
• Non-interactive — running as the distill subagent (for example inside the Allium loop), where no user is reachable. Scope the distillation from the goal you were given, and do not guess at judgement calls: record each unconfirmed judgement — intended vs accidental behaviour, actor identity, candidate processes, scope exclusions — as an open question declaration in the distilled spec, and list the parked questions in your final output.

Scoping the distillation effort

Before diving into code, establish what you are trying to specify. Not every line of code deserves a place in the spec.

Questions to ask first

1. "What subset of this codebase are we specifying?"

Mono repos often contain multiple distinct systems. You may only need a spec for one service or domain. Clarify boundaries explicitly before starting.

1. "Is there code we should deliberately exclude?"

• Legacy code: features kept for backwards compatibility but not part of the core system
• Incidental code: supporting infrastructure that is not domain-level (logging, metrics, deployment)
• Deprecated paths: code scheduled for removal
• Experimental features: behind feature flags, not yet design decisions

1. "Who owns this spec?"

Different teams may own different parts of a mono repo. Each team's spec should focus on their domain.

The "Would we rebuild this?" test

For any code path you encounter, ask: "If we rebuilt this system from scratch, would this be in the requirements?"

• Yes: include in spec
• No, it is legacy: exclude
• No, it is infrastructure: exclude
• No, it is a workaround: exclude (but note the underlying need it addresses)

Documenting scope decisions

At the top of a distilled spec, document what is included and excluded:

-- allium: 3
-- interview-scheduling.allium
 
-- Scope: Interview scheduling flow only
-- Includes: Candidacy, Interview, InterviewSlot, Invitation, Feedback
-- Excludes:
--   - User authentication (use auth library spec)
--   - Analytics/reporting (separate spec)
--   - Legacy V1 API (deprecated, not specified)
--   - Greenhouse sync (use greenhouse library spec)

The version marker (-- allium: N) must be the first line of every .allium file. Use the current language version number.

Finding the right level of abstraction

Distillation and elicitation share the same fundamental challenge: choosing what to include. The tests below work in both directions, whether you are hearing a stakeholder describe a feature or reading code that implements it.

The "Why" test

For every detail in the code, ask: "Why does the stakeholder care about this?"

If you cannot articulate why a stakeholder would care, it is probably implementation.

The "Could it be different?" test

Ask: "Could this be implemented differently while still being the same system?"

• If yes: probably implementation detail, abstract it away
• If no: probably domain-level, include it

The "Template vs Instance" test

Is this a category of thing, or a specific instance?

Sometimes the instance IS the domain concern. See "The concrete detail problem" below.

The distillation mindset

Code is over-specified

Every line of code makes decisions that might not matter at the domain level:

# Code tells you:
def send_invitation(candidate_id: int, slot_ids: List[int]) -> Invitation:
    candidate = db.session.query(Candidate).get(candidate_id)
    slots = db.session.query(InterviewSlot).filter(
        InterviewSlot.id.in_(slot_ids),
        InterviewSlot.status == 'confirmed'
    ).all()
    invitation = Invitation(
        candidate_id=candidate_id,
        token=secrets.token_urlsafe(32),
        expires_at=datetime.utcnow() + timedelta(days=7),
        status='pending'
    )
    db.session.add(invitation)
    for slot in slots:
        slot.status = 'proposed'
        invitation.slots.append(slot)
    db.session.commit()
    send_email(
        to=candidate.email,
        template='interview_invitation',
        context={'invitation': invitation, 'slots': slots}
    )
    return invitation

-- Specification should say:
rule SendInvitation {
    when: SendInvitation(candidacy, slots)
 
    requires: slots.all(s => s.status = confirmed)
 
    ensures:
        for s in slots:
            s.status = proposed
    ensures: Invitation.created(
        candidacy: candidacy,
        slots: slots,
        expires_at: now + 7.days,
        status: pending
    )
    ensures: Email.created(
        to: candidacy.candidate.email,
        template: interview_invitation
    )
}

What we dropped:

• candidate_id: int became just candidacy
• db.session.query(...) became relationship traversal
• secrets.token_urlsafe(32) removed entirely (token is implementation)
• datetime.utcnow() + timedelta(...) became now + 7.days
• db.session.add/commit implied by created
• invitation.slots.append(slot) implied by relationship

Ask "Would a product owner care?"

For every detail in the code, ask:

Distinguish means from ends

Means: how the code achieves something. Ends: what outcome the system needs.

The concrete detail problem

The hardest judgement call: when is a concrete detail part of the domain vs just implementation?

Google OAuth example

You find this code:

OAUTH_PROVIDERS = {
    'google': GoogleOAuthProvider(client_id=..., client_secret=...),
}
def authenticate(provider: str, code: str) -> User:
    return OAUTH_PROVIDERS[provider].authenticate(code)

Question: Is "Google OAuth" domain-level or implementation?

It is implementation if:

• Google is just the auth mechanism chosen
• It could be replaced with any OAuth provider
• Users do not see or care which provider
• The code is written generically (provider is a parameter)

It is domain-level if:

• Users explicitly choose Google (vs Microsoft, etc.)
• "Sign in with Google" is a feature
• Google-specific scopes or permissions are used
• Multiple providers are supported as a feature

How to tell: Look at the UI and user flows. If users see "Sign in with Google" as a choice, it is domain-level. If they just see "Sign in" and Google happens to be behind it, it is implementation.

Database choice example

You find PostgreSQL-specific code:

from sqlalchemy.dialects.postgresql import JSONB, ARRAY
class Candidate(Base):
    skills = Column(ARRAY(String))
    metadata = Column(JSONB)

Almost always implementation. The spec should say:

entity Candidate {
    skills: Set<String>
    metadata: String?              -- or model specific fields
}

The specific database is rarely domain-level. Exception: if the system explicitly promises PostgreSQL compatibility or specific PostgreSQL features to users.

Third-party integration example

You find Greenhouse ATS integration:

class GreenhouseSync:
    def import_candidate(self, greenhouse_id: str) -> Candidate:
        data = self.client.get_candidate(greenhouse_id)
        return Candidate(
            name=data['name'],
            email=data['email'],
            greenhouse_id=greenhouse_id,
            source='greenhouse'
        )

Could be either:

Implementation if:

• Greenhouse is just where candidates happen to come from
• Could be swapped for Lever, Workable, etc.
• The integration is an implementation detail of "candidates are imported"

Spec:

external entity Candidate {
    name: String
    email: String
    source: CandidateSource
}

Product-level if:

• "Greenhouse integration" is a selling point
• Users configure their Greenhouse connection
• Greenhouse-specific features are exposed (like syncing feedback back)

Spec:

external entity Candidate {
    name: String
    email: String
    greenhouse_id: String?  -- explicitly modeled
}
 
rule SyncFromGreenhouse {
    when: GreenhouseWebhookReceived(candidate_data)
    ensures: Candidate.created(
        ...
        greenhouse_id: candidate_data.id
    )
}

The "Multiple implementations" heuristic

Look for variation in the codebase:

• If there is only one OAuth provider, probably implementation
• If there are multiple OAuth providers, probably domain-level
• If there is only one notification channel, probably implementation
• If there are Slack AND email AND SMS, probably domain-level

The presence of multiple implementations suggests the variation itself is a domain concern.

Distillation process

Distillation reads a lot of code but produces a small spec. The expensive mistake is letting all that source pile up in one context window where it is re-read on every turn. Keep the working set lean: orchestrate the read-heavy steps as subagents and keep only their distilled output.

The orchestration model

For anything beyond a handful of files, do not read the whole codebase yourself. Instead: