vikingmute

vikingmute / review-forge

Public

review-forge is an Agent Skill for structured, auditable code review workflows

59
7
85% credibility
Found Jun 01, 2026 at 59 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

Review Forge is an AI-powered code review skill that orchestrates multi-pass reviews from different AI models, synthesizes findings into actionable fix checklists, lets humans approve which fixes to apply, and verifies that implemented changes actually work.

How It Works

1
🔍 You discover a better way to review code

You find Review Forge, a tool that helps your AI coding assistant review code more thoroughly by getting multiple perspectives on your changes.

2
⚙️ You connect it to your AI assistant

You install Review Forge so your AI coding assistant knows how to run structured, multi-pass code reviews with human approval checkpoints.

3
🤖 Your AI reviews your code changes

You ask your assistant to review a feature branch, and it creates detailed review files examining your code from different angles.

4
📋 All findings come together

The assistant synthesizes all the review perspectives into one clear checklist of issues and suggested fixes, organized by feature.

5
You choose what to fix
💡
Approve fixes and implement them

Your assistant applies the checked fixes and automatically runs tests to make sure everything still works

📝
Skip implementation for now

You save the checklist for later and continue working on other things

6
🔬 Everything gets verified

The assistant independently checks that the fixes actually solved the problems and updates your status file with the results.

🎉 Your code is cleaner and safer

You've completed a thorough, auditable review process with multiple AI perspectives, human approval, and verified fixes—all organized neatly in one place.

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AI-Generated Review

What is review-forge?

Review-forge is an agent skill that brings structure and accountability to AI-assisted code reviews. Instead of a one-shot LLM analysis, it orchestrates a multi-pass workflow: the tool runs reviews from multiple perspectives, synthesizes findings into a single fix checklist, lets humans approve which issues to address, then implements and verifies those fixes with regression tests. It works with local changes, branches, PRs, or commit ranges, and stores all review artifacts in a dedicated folder so nothing pollutes your codebase.

Why is it gaining traction?

The key insight here is the human-in-the-loop approval step. Most AI review tools either overwhelm you with findings or silently fix things without asking. Review-forge uses checkboxes as a deliberate approval boundary, so you decide what gets touched. The multi-agent synthesis is also clever: instead of managing three different review outputs manually, the tool merges them into one actionable checklist. The workflow commands (review, synthesize, fix, verify) map cleanly to how developers actually think about the review process.

Who should use this?

Teams using AI coding assistants like Codex, Cursor, or OpenCode who want more control over what the AI changes. It's especially useful for codebases where audit trails matter or where you need to justify why certain fixes were chosen. Individual developers who find themselves manually copying AI review output between tools will appreciate the automation. It's probably overkill for quick one-off reviews, but valuable for features or refactors where thoroughness beats speed.

Verdict

Review-forge solves a real problem with a thoughtful design, but at 59 stars it's early-stage and the "Unknown" language designation raises questions about production readiness. The 0.85% credibility score reflects this novelty. Worth experimenting with if you live inside AI-assisted workflows, but hold off on mission-critical use until the project matures and documentation expands.

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