/distill

name: distill
description: "Extract an Allium specification from an existing codebase. Use when the user has existing code and wants to distil behaviour into a spec, reverse engineer a specification from implementation, generate a spec from code, turn implementation into a behavioural specification, or document what a codebase does in Allium terms."

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.

2. **"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

3. **"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?"

| Code detail | Why? | Include? | |-------------|------|----------| | Invitation expires in 7 days | Affects candidate experience | Yes | | Token is 32 bytes URL-safe | Security implementation | No | | Sessions stored in Redis | Performance choice | No | | Uses PostgreSQL JSONB | Database implementation | No | | Slot status changes to 'proposed' | Affects what candidate sees | Yes | | Email sent when invitation accepted | Communication requirement | Yes |

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

| Detail | Could be different? | Include? | |--------|---------------------|----------| | `secrets.token_urlsafe(32)` | Yes, any secure token generation | No | | 7-day invitation expiry | No, this is the design decision | Yes | | PostgreSQL database | Yes, any database | No | | "Pending, Confirmed, Completed" states | No, this is the workflow | Yes |

The "Template vs Instance" test

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

| Instance (often implementation) | Template (often domain-level) | |--------------------------------|-------------------------------| | Google OAuth | Authentication provider | | Slack webhook | Notification channel | | SendGrid API | Email delivery | | `timedelta(hours=3)` | Confirmation deadline |

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 = Invi