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forge-review

Orchestrate multi-agent code reviews and generate severity-tiered Markdown reports.

3|2|Updated Apr 3, 2026
Search Tags:#performance#multi-agent#code-review#data-structures#report-generation#complexity-analysis#pipeline-handoff
One-click install
npx skills add https://github.com/fluxforgeai/ARTEMIS --skill forge-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forge-review
Source: https://github.com/fluxforgeai/ARTEMIS/tree/main/.claude/skills/forge-review
Command: npx skills add https://github.com/fluxforgeai/ARTEMIS --skill forge-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The forge-review skill orchestrates a coordinated, read-only code review across a repository to detect algorithmic complexity issues, inappropriate data structures, paradigm inconsistencies, and performance anti-patterns, removing the manual overhead of deep, multi-dimensional reviews.

Core Features & Use Cases

  • Multi-agent orchestration: Launches four specialist subagents (Complexity Analyst, DS&A Reviewer, Paradigm Enforcer, Efficiency Sentinel) in parallel and aggregates structured findings.
  • Deduplication & severity tiers: Merges overlapping findings, assigns Critical/Warning/Suggestion tiers, and numbers items for clear triage.
  • Pipeline integration: Reads loop state for lifecycle handoff, writes structured Markdown reports to docs/reviews/, and enforces read-only guarantees on source files.
  • Use Case: Run a diff-scoped review after an implementation loop to produce a prioritized review report for triage or to run a full-codebase audit prior to release.

Quick Start

Run /forge-review to review changed files (diff scope) and produce a severity-tiered Markdown report saved to docs/reviews/.

Frequently Asked Questions about forge-review

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

FAQPage Schema
How do I run a multi-agent code review for algorithmic complexity and performance anti-patterns?▼

Run a multi-agent code review by orchestrating four specialist subagents that analyze source code in parallel for complexity issues, data structure mismatches, and performance anti-patterns, producing a deduplicated severity-tiered Markdown report.

What is the best way to automate code reviews before a release without modifying source files?▼

Automated code reviews can run a full-codebase audit using read-only agents that analyze paradigm violations and efficiency issues, ensuring source files remain unmodified while generating structured Markdown reports for triage.

How does severity tiering work for code review findings?▼

Severity tiering assigns Critical, Warning, or Suggestion levels to deduplicated code review findings, numbering each item to provide clear triage prioritization for algorithmic complexity and performance anti-patterns detected across the repository.

Can I scope a code review to only the changed files in a diff?▼

Yes, code reviews support diff scope to analyze only changed files within an implementation loop, as well as full-codebase scope for comprehensive audits across common programming languages and filtered file sets.

Does pipeline integration support lifecycle handoff for automated code review reports?▼

Pipeline integration reads loop state for lifecycle handoff and writes structured Markdown reports directly to the docs/reviews/ directory, enabling seamless downstream triage and continuous integration workflows.

What are the limitations of using multi-agent code reviews for complexity analysis?▼

Multi-agent code reviews are restricted to read-only analysis of source files and can only write output reports to docs/reviews/, meaning they cannot automatically apply fixes or modify code directly within the repository.

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