arbe-mull

Runs a six-stage design workflow from clarified request to implementation plan with review gates.

Updated May 16, 2026
One-click install
npx skills add https://github.com/oskarrough/robots --skill arbe-mull-oskarrough
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arbe-mull
Source: https://github.com/oskarrough/robots/tree/main/skills/mull
Command: npx skills add https://github.com/oskarrough/robots --skill arbe-mull-oskarrough

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Jumping straight into code on an unfamiliar or cross-cutting change lets bad assumptions compound across files. This Skill enforces a structured thinking process — clarify, questions, research, design, outline, plan — so every architectural decision is grounded in verified codebase facts before any code is written. ## Core Features & Use Cases - Six gated stages: Each stage (clarify → questions → research → design → outline → plan) produces an artifact that must pass a check verdict before the next begins. - Ticket isolation for research: The research stage never sees the original request, producing an unbiased account of how the codebase actually works today, with file:line evidence for every claim. - Subagent delegation: Stages and checks can run in fresh subagents, with research fanned out across 2–6 parallel agents grouped by codebase area. - Use Case: Before adding image upload to an editor spanning storage, API, and UI layers, run a mull session to research current patterns, resolve design decisions with the user, and produce a phased implementation plan with runnable verification commands. ## Quick Start Ask the AI to run a mull session on your feature idea or spec, choosing whether stage documents are written to disk under .mull/ or kept in chat.

Frequently Asked Questions about arbe-mull

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I plan a feature before writing code with an AI agent?▼

Run a mull session: it walks through clarify, research questions, codebase research, design discussion, outline, and plan stages, each gated by a review check. The output is a phased implementation plan with concrete file changes and runnable verification commands.

How does the mull workflow keep codebase research unbiased?▼

The research stage uses ticket isolation: it reads only the research questions, never the original request or clarified intent. This prevents findings from being shaped by the desired solution, and every claim must carry a file:line citation.

When should I use a multi-stage design workflow versus coding directly?▼

Use it for unfamiliar code, cross-cutting features, data-model changes, or async and UI state work where a bad assumption compounds across files. Skip it for small, obvious changes — the workflow itself says to say so and move on.

Does the mull skill write or modify any code?▼

No. Mull stops at the implementation plan and never implements. After the plan passes review, you create a branch or worktree and hand the artifacts to a separate implementing agent that works phase by phase.

Can mull stages run in subagents or parallel?▼

Yes. If the runtime supports subagents, each stage and check runs in a fresh one, and research fans out to 2–6 parallel subagents grouped by codebase area. Without subagents it runs in a single context with the same structure.

What happens when a stage output fails the review gate?▼

The check emits a verdict of pass, revise, or go_back. Revise fixes the current stage with what it has; go_back returns to an earlier stage when the gap is upstream, such as thin research blocking a design decision.