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\nconfidence scoring + cross-correlation"];
    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;
}

---

Phase 1: Gather MR Data

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

If MCP tools are available (plugin install), use the single tool call:

mcp__omniforge__fetch_mr_data(mr_id="{id}", repo_root="{cwd}")

Returns a structured JSON package with: mr_id, title, author, source_branch, target_branch, pipeline_status, description, comments, diff, diff_line_count, diff_too_large, diff_truncated, diff_line_map, commits, files_changed, labels, assignees, reviewers.

IMPORTANT: diff_line_map is already included in this response. It contains exact changed line numbers per file (added_lines, all_new_lines, hunks). When posting inline threads, use these line numbers directly — do NOT call map_diff_lines separately. The standalone map_diff_lines tool is only for re-parsing if you have a diff string from another source.

If diff_too_large is true, the diff is auto-truncated to 10,000 lines. Agents explore full files in their worktrees instead.

Error handling: If the tool returns success: false, check error_type:

• auth_failure — Tell user to run glab auth login
• mr_not_found — Verify MR number and repository
• validation_error — Check input format

Fallback (personal skill install without MCP server): Use bash commands directly:

glab auth status
glab mr view {id} -F json
glab mr view {id} -c
glab mr diff {id} --raw
git fetch origin {source_branch} {target_branch}
git log --oneline origin/{target_branch}..origin/{source_branch}

Large Diff Strategy

When diff_line_count is high (>3000 lines) or diff_truncated is true:

1. Don't inject the full diff into agent prompts. Save the diff to a temp file and give agents the file path. They can read sections as needed.
2. Provide a diff summary instead. Use files_changed and diff_line_map to create a per-file summary table:

   | File | Added Lines | Hunks |
   |------|-------------|-------|
   | src/app.py | 42 | 3 |
   | tests/test_app.py | 28 | 2 |
   

3. Agents explore in worktrees. With the summary + worktree access, agents can read full files and understand context without the raw diff consuming their context window.
4. Save diff to temp file pattern:

   # Save diff for agents to read on-demand
   echo "$DIFF_TEXT" > /tmp/omni_mr{id}_diff.txt
   # Give agents the path, not the content
   

5. Clean up temp files in Phase 7 alongside worktree cleanup.

This approach reduces agent context usage by 50-80% on large MRs while preserving full review quality.

---

Phase 2: Create 3 Worktrees

REQUIRED SUB-SKILL: Follow superpowers:using-git-worktrees pattern.

If MCP tools are available (plugin install), use the single tool call:

mcp__omniforge__create_review_worktrees(
    mr_id="{id}",
    source_branch="{from Phase 1 response}",
    repo_root="{cwd}"
)

Returns absolute paths for all 3 worktrees:

• worktrees.analyst — MR Analyst (OmniForge)
• worktrees.codebase — Codebase Reviewer (OmniForge)
• worktrees.security — Security Reviewer (OmniForge)

The tool automatically: ensures .worktrees/ exists and is gitignored, cleans stale worktrees from crashed runs, fetches the source branch, creates 3 detached worktrees, resolves absolute paths.

Error handling: If creation fails partway, the tool auto-cleans any partially created worktrees and returns cleanup_performed: true.

Fallback (personal skill install without MCP server):

mkdir -p .worktrees
git check-ignore -q .worktrees 2>/dev/null || echo ".worktrees/" >> .gitignore
git worktree remove .worktrees/omni-analyst-{id} --force 2>/dev/null
git worktree remove .worktrees/omni-codebase-{id} --force 2>/dev/null
git worktree remove .worktrees/omni-security-{id} --force 2>/dev/null
git worktree prune
git fetch origin {source_branch}
git worktree add .worktrees/omni-analyst-{id} origin/{source_branch} --detach
git worktree add .worktrees/omni-codebase-{id} origin/{source_branch} --detach
git worktree add .worktrees/omni-security-{id} origin/{source_branch} --detach
ANALYST_PATH=$(cd .worktrees/omni-analyst-{id} && pwd)
CODEBASE_PATH=$(cd .worktrees/omni-codebase-{id} && pwd)
SECURITY_PATH=$(cd .worktrees/omni-security-{id} && pwd)

---

Phase 3: Dispatch 3 OmniForge Agents in Parallel

REQUIRED SUB-SKILL: Use superpowers:dispatching-parallel-agents pattern.

Dispatch all 3 agents simultaneously using the Agent tool (NOT TaskCreate — the Agent tool spawns subagents). Send a single message with 3 parallel Agent tool calls. Each agent gets:

• Full MR context package (injected, NOT fetched by agent)
• Its own worktree absolute path for exploration
• Agent-specific review prompt (from template file)
• MR comments/discussions (all 3 agents, not just the MR Analyst)
• Confidence scoring instructions

Agent 1: MR Analyst (OmniForge)

• Template: ./references/mr-analyst-prompt.md
• Worktree: .worktrees/omni-analyst-{id}
• Focus: Process quality — commit-by-commit analysis, MR description, discussions, scope

Agent 2: Codebase Reviewer (OmniForge)

• Template: ./references/codebase-reviewer-prompt.md
• Worktree: .worktrees/omni-codebase-{id}
• Focus: Code quality — architecture, logic, testing, patterns, DRY, performance

Agent 3: Security Reviewer (OmniForge)

• Template: ./references/security-reviewer-prompt.md
• Worktree: .worktrees/omni-security-{id}
• Focus: Security — OWASP Top 10, secrets, auth/authz, injection, data exposure

Model selection: Use the most capable model available (opus) for all 3 agents. Review requires judgment, not just mechanical work.

Agent Prompt Construction

For each agent, fill the template placeholders:

• {MR_ID} — MR number
• {MR_TITLE} — MR title from JSON
• {WORKTREE_PATH} — Absolute path to agent's worktree (convert from relative)
• {MR_JSON_DATA} — Full JSON metadata (all 3 agents get this)
• {MR_DESCRIPTION} — MR description text
• {MR_COMMENTS} — All discussion threads (all 3 agents get this — discussions may contain security/code context)
• {MR_DIFF} — Raw diff output
• {COMMIT_LIST} — Commit SHAs and messages
• {FILES_CHANGED_LIST} — List of changed file paths
• {SOURCE_BRANCH} — MR source branch name
• {TARGET_BRANCH} — MR target branch name

---

Phase 4: Consolidation

Threshold: 70. Only findings with confidence >= 70 appear in the final report.

REQUIRED REFERENCE: ./references/consolidation-guide.md — you MUST read this before consolidating. Contains the full algorithm: confidence scoring, cross-correlation (+15 for 2 agents, +25 for 3), false positive auto-reduction (-30), deduplication, verdict logic, report template, and agent agreement matrix. Do NOT attempt consolidation from memory — the algorithm has specific rules that must be followed exactly.

---

Phase 5: Present Report

## OmniForge Report: !{id} — {title}

**Branch:** {source} → {target} | **Author:** {author} | **Pipeline:** {status}

### Summary
[1-3 sentence executive summary: overall assessment, key risks]

### Verdict: [APPROVE | APPROVE_WITH_FIXES | REQUEST_CHANGES | BLOCK]

### Strengths
[Specific things done well with file:line references]

### Issues

#### Critical (Must Fix Before Merge)
[Bugs, security vulnerabilities, data loss risks]
Each: file:line | description | why it matters | how to fix | confidence | source agent(s)

#### Important (Should Fix)
[Architecture problems, missing edge cases, test gaps]

#### Minor (Nice to Have)
[Optimizations, documentation, code clarity]

### MR Process Notes
[From MR Analyst (OmniForge): commit quality, description, unresolved discussions]

### Security Assessment
[From Security Reviewer (OmniForge): OWASP findings, posture assessment]

### Recommendations
[Future improvements beyond this MR's scope]

### Agent Agreement Matrix
| File/Area | MR Analyst (OmniForge) | Codebase (OmniForge) | Security (OmniForge) | Consensus |

CRITICAL: Present the report FIRST. Never auto-post anything.

---

Phase 6: Action Menu

After presenting the full report, offer structured actions:

What would you like to do?

1. Full review post — summary comment + inline discussion threads (Recommended)
2. Post summary only (overview comment, no inline threads)
3. Post inline findings only (discussion threads, no summary)
4. Create GitLab issues for Critical/Important items
5. Approve the MR
6. Open MR in browser
7. Re-review a specific area (single focused agent)
8. Verify a specific concern (run a targeted check)
9. Done — no action needed

Wait for user choice. Execute chosen action(s). Return to menu until user selects "Done".

Option 1: Full Review Post (Recommended)

Post summary comment + individual inline threads for each finding >= 70 confidence. Each finding gets its own thread for independent resolution.

REQUIRED REFERENCE: ./references/posting-guide.md — you MUST read this before posting anything. Contains the summary comment template, inline thread template, MCP tool call syntax (post_full_review findings JSON format), and bash fallback commands. Do NOT improvise posting format — use the exact templates from the reference.

---

Phase 7: Cleanup

ALWAYS runs, regardless of success or failure.

If MCP tools are available:

mcp__omniforge__cleanup_review_worktrees(mr_id="{id}", repo_root="{cwd}")

The tool force-removes all 3 worktrees, cleans leftover directories, and prunes git worktree references. Reports what was removed vs. already clean.

Fallback (personal skill install without MCP server):

git worktree remove .worktrees/omni-analyst-{id} --force 2>/dev/null
git worktree remove .worktrees/omni-codebase-{id} --force 2>/dev/null
git worktree remove .worktrees/omni-security-{id} --force 2>/dev/null
rm -rf .worktrees/omni-analyst-{id} .worktrees/omni-codebase-{id} .worktrees/omni-security-{id} 2>/dev/null
git worktree prune

---

Error Handling

Error	Response
glab not authenticated	"Run glab auth login first." Stop.
MR not found	"MR !{id} not found. Verify the number and repository." Stop.
Network failure	Retry glab command once. If still fails, report error and stop.
Worktree creation fails	Try with timestamp suffix. If still fails, clean up and stop.
1 agent fails	Continue with 2/3. Note gap in report.
2+ agents fail	Present partial results. Suggest manual review.
Diff too large	Summarize diff. Give agents file list to explore in worktrees.
Cleanup fails	Force remove directories. Report if still stuck.

Cleanup guarantee: The entire flow is wrapped in a try/finally pattern. Phase 7 runs no matter what.

---

Quick Reference

Phase	What	How
1. Gather	Fetch MR data	glab mr view/diff (JSON + raw)
2. Setup	Create 3 worktrees	git worktree add --detach (×3)
3. Review	Dispatch OmniForge agents	Agent tool parallel (×3, opus model)
4. Merge	Consolidate findings	Confidence score + cross-correlate + dedup
5. Report	Present OmniForge report	Structured markdown with verdict
6. Act	User chooses	glab mr note/approve, glab issue create
7. Clean	Remove worktrees	git worktree remove --force (×3) + prune

---

The "Small MR" Trap

EVERY MR gets the full OmniForge process. No exceptions.

The most dangerous rationalization is: "This MR is small/simple, I'll just do a quick review." This is exactly when issues get missed — when you assume simplicity means safety.

Baseline testing proved this. Without OmniForge, an agent reviewing a "simple" 86-line CI/CD change:

• Skipped worktree isolation ("unnecessary overhead")
• Skipped parallel agents ("scope was small enough")
• Skipped confidence scoring ("informal severity labels are fine")
• Read the wrong branch (local instead of MR source)
• Did no systematic security check (just "no issues found")

A CI/CD file change can expose secrets, break production deployments, or modify security configurations. "Small" does not mean "safe."

The rule: Every MR gets 3 OmniForge agents, 3 worktrees, confidence scoring, and the full report. The overhead is minutes. The cost of a missed issue is hours or days.

---

Red Flags — STOP and Follow the Process

Thought	Reality
"This MR is too small for full review"	Small MRs in CI/CD, auth, or config are the most dangerous. Full process.
"I'll just read the diff, no worktree needed"	The diff is insufficient. You need full file context. Create worktrees.
"One agent can handle this"	You'll miss cross-perspective insights. Dispatch all 3.
"I'll skip confidence scoring, findings are obvious"	"Obvious" findings are often false positives. Score everything.
"Let me just post my comments now"	Present the report first. User decides what to post.
"I can use the local branch instead of MR source"	The local branch may be stale or different. Always fetch and use MR source.
"Security review isn't needed for this change"	Every change has security implications. Always run the security agent.
"Cleanup can wait"	Stale worktrees accumulate. Clean up immediately.
"The MR comments aren't relevant for code/security review"	Discussions often contain security context, design decisions, and known risks. Inject comments into ALL agents.
"Relative paths are fine for worktrees"	Agents need absolute paths to reliably navigate. Always convert to absolute.
"I'll use TaskCreate to track the agents"	TaskCreate tracks YOUR work. Use the Agent tool to dispatch subagents. Different tools, different purposes.

All of these mean: Follow the 7-phase OmniForge process. No shortcuts.

---

Never

• Use gh (this is GitLab — use glab exclusively)
• Push to origin (user's rule: never push without explicit permission)
• Post comments without user approval from the action menu
• Approve MR without explicit user request
• Skip worktree cleanup (even on failure)
• Include findings below confidence threshold 70
• Let agents share worktrees (isolation is critical)
• Add AI attribution to posted comments (no "Generated by Claude" etc.)
• Skip any of the 7 phases for any reason

Always

• Fetch MR data in Phase 1 before dispatching agents
• Create isolated worktrees per agent on the MR source branch
• Inject context into agent prompts (don't make agents re-fetch)
• Use confidence scoring with threshold 70
• Cross-correlate findings across agents
• Present full OmniForge report before any action
• Ask user which actions to take via the action menu
• Clean up all worktrees regardless of outcome
• Use glab for all GitLab operations

---

Integration

Uses:

• superpowers:using-git-worktrees — Worktree setup/teardown pattern
• superpowers:dispatching-parallel-agents — Parallel dispatch pattern
• superpowers:requesting-code-review — Review output format (Strengths/Issues/Assessment)
• superpowers:verification-before-completion — Evidence-based findings

OmniForge Agent Templates:

• ./references/mr-analyst-prompt.md — MR Analyst (OmniForge)
• ./references/codebase-reviewer-prompt.md — Codebase Reviewer (OmniForge)
• ./references/security-reviewer-prompt.md — Security Reviewer (OmniForge)
• ./references/consolidation-guide.md — Cross-correlation and report format