distill

An automated worker that extracts an Allium specification from existing code. Allium is a language for describing software behaviour at the domain level rather than its implementation.

In plain words
What is it for?
It is for non-interactive reverse engineering of software behaviour, returning the specification location, a summary, and unresolved questions.
Why use it?
It lets a larger workflow inspect the code without putting that source into the caller's conversation. When the code leaves something unclear, it reports the issue as an open question.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/juxt/allium/distill
Clone the repo
git clone --depth 1 https://github.com/juxt/allium
Per session 63 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 7,383 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00063 $0.07383
Opus 5 $0.00032 $0.03691
Sonnet 5 $0.00013 $0.01477
Haiku 4.5 $0.00006 $0.00738

Measured 2d ago against content hash b1fbb4adfb4a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

distill scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Sends data to an external URLmediumData exfiltration

A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.

| `requests.post('https://slack.com/api/...')` | `Notification.created(channel: slack)` |

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| `requests.post('https://slack.com/api/...')` | `Notification.created(channel: slack)` |
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • distill — 100% identical, 0 lines differ
.github/agents/distill.agent.md · 865 lines

How it starts

The opening of the file, as written. The whole thing — 865 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Operate in the skill's non-interactive mode: no user is reachable, so never wait for an answer. Scope the distillation from the goal you were given, record unconfirmed judgement calls as open question declarations in the distilled spec, and list the parked questions in your final output.

Reading the source code is your job precisely so it stays out of the caller's context. Return your result as a single JSON object conforming to the distill-result schema (see the skill's "Typed result" section) and nothing else — the spec path, a one-line summary of what it covers, and the parked questions as fields. Not the code you read, and no prose around the object.

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

Read the full file on GitHub · 865 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 865 lines · 63 tokens per session scan B b1fbb4adfb4a

Subscribe to this mod's changes

distill is an agent published in the GitHub repository juxt/allium (477 stars, last pushed 6d ago), licensed MIT. It adds 63 tokens to every session and 7,383 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.