dg-skill

Orchestrates two sub-agents to critique and defend code until convergence.

494|47|Updated Mar 10, 2026
One-click install
npx skills add https://github.com/agentspan-ai/agentspan --skill dg-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dg-skill
Source: https://github.com/agentspan-ai/agentspan/tree/main/sdk/python/tests/fixtures/skills/dg-skill
Command: npx skills add https://github.com/agentspan-ai/agentspan --skill dg-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Adversarial code review with two sub-agents helps teams automatically critique and defend code, reducing review time and increasing coverage.

Core Features & Use Cases

  • Two-agent adversarial review rounds (Gilfoyle and Dinesh) that iterate until convergence.
  • Template-driven output generation using comic-template.html to present findings.
  • Suitable for reviewing pull requests, security-sensitive changes, and complex refactors.

Quick Start

Dispatch Gilfoyle to review the code, then Dinesh to respond, and iterate until convergence using comic-template.html as the output template.

Frequently Asked Questions about dg-skill

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

FAQPage Schema
What is adversarial code review and how does it work with multiple agents?▼

Adversarial code review is an automated process where two sub-agents iterate by critiquing and defending code changes. This multi-agent approach continues rounds of dialogue until convergence is reached, providing multiple perspectives on software quality and security.

How do I automate iterative code review for pull requests?▼

You can automate iterative code review by dispatching two sub-agents to critique and respond to code changes. The agents iterate through dialogue rounds until convergence, generating template-driven output to present review findings for your pull requests.

Can multi-agent code review be used for security-sensitive changes?▼

Multi-agent code review is suitable for security-sensitive changes and complex refactors. The two-agent adversarial approach provides multiple perspectives, which is beneficial when reviewing quality-critical scenarios where thorough coverage and iterative dialogue are required.

What's the best way to present code review findings from multiple agents?▼

The best way to present multi-agent code review findings is through template-driven output generation. This Skill uses a comic-template HTML file to format and display the converged results from the adversarial review rounds between the two sub-agents.

When should I use convergence-based stopping for code review?▼

Convergence-based stopping should be used when running iterative adversarial review rounds between multiple agents. It automatically halts the critique and defense dialogue once the sub-agents reach a consensus, preventing endless loops and finalizing the review output.