omnireview-gitlab

OmniForge

Multi-agent adversarial MR review — 3 parallel agents, 3 worktrees, 1 consolidated report.

Dispatch 3 parallel specialized agents in isolated git worktrees to perform adversarial, independent analysis of a GitLab MR. Consolidate findings via confidence scoring, present actionable report, then offer structured actions (comment, create issues, approve).

Core principle: Independent adversarial review + confidence filtering + worktree isolation = high-signal feedback with minimal noise.

Announce at start: "I'm using OmniForge to review MR !{id}."

Prerequisites

• glab CLI authenticated (glab auth status to verify)
• Git repository with remote pointing to GitLab
• Current working directory is in the git repo

Input Parsing

Accept any of: MR number (136), prefixed (!136), or full GitLab URL. Extract MR ID. If URL provided, extract project path and MR IID.

The Process

digraph omnireview_flow {
    rankdir=TB;
    node [shape=box];

    gather [label="Phase 1: Gather MR Data\nglab mr view + diff + comments"];
    worktrees [label="Phase 2: Create 3 Worktrees\n(on MR source branch)"];
    dispatch [label="Phase 3: Dispatch 3 OmniForge Agents\nIN PARALLEL"];
    analyst [label="MR Analyst (OmniForge)\n(worktree 1)"];
    codebase [label="Codebase Reviewer (OmniForge)\n(worktree 2)"];
    security [label="Security Reviewer (OmniForge)\n(worktree 3)"];
    consolidate [label="Phase 4: Consolidation\nconsolidator + worklist"];
    report [label="Phase 5: Present Report\n(NEVER auto-post)"];
    action [label="Phase 6: Action Menu\n(user chooses)"];
    cleanup [label="Phase 7: Cleanup\n(ALWAYS runs)"];

    gather -> worktrees -> dispatch;
    dispatch -> analyst;
    dispatch -> codebase;
    dispatch -> security;
    analyst -> consolidate;
    codebase -> consolidate;
    security -> consolidate;
    consolidate -> report -> action -> cleanup;
}

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Phase 1: Gather MR Data

Fetch ALL data before dispatching agents. Agents get data injected — they never re-fetch.