Convene seven AI personas to review system designs and generate architectural reports.
npx skills add https://github.com/ksmeltzer/ARC-7 --skill arc-7
Please help me install this Agent Skill. Skill: ARC-7 Source: https://github.com/ksmeltzer/ARC-7/tree/main/skills/ARC-7 Command: npx skills add https://github.com/ksmeltzer/ARC-7 --skill arc-7
Convene an elite architectural decision panel using 7 specialized AI personas — each running on an optimal GitHub Copilot model. It supports three modes: reviewing the current conversation context, reviewing a specific document/proposal, or reviewing a full codebase from a GitHub URL. The panel applies multi-model cognitive diversity, structured debate with governance, blind voting for critical decisions, and formal conflict resolution — grounded in ensemble learning principles and adversarial review dynamics.
Invoke ARC-7 to review the current conversation, a document, or a GitHub codebase.
High-intent search queries and answers about installing and using this skill.
To get an AI architectural review for a full GitHub codebase, you can use a multi-model panel to assess the repository directly from its GitHub URL. This convenes specialized AI personas to evaluate system design, security, and API structure.
You can run a structured design review on a specific document by passing it to an AI review panel. The panel uses governance, formal conflict resolution, and synthesized reporting to validate the proposal's architecture and product value.
Yes, you can use multi-model AI debate to evaluate your current system design context. The review panel supports analyzing the active conversation context directly, applying adversarial review dynamics and ensemble learning principles to validate architecture.
The best way to resolve architectural conflicts during a codebase review is through a panel using formal conflict resolution and blind voting. This approach forces specialized AI personas to debate governance and synthesis rules until a consensus is reached.
Multi-model cognitive diversity improves security and API design assessments by leveraging ensemble learning principles. Running specialized AI personas on optimal models ensures adversarial review dynamics catch flaws that a single model might miss.
You should use a seven-person AI panel instead of a single model for system design validation when you need enterprise-grade governance, blind voting, and structured debate. It provides synthesized architectural reports grounded in adversarial review dynamics.
JacksonZx535Review architectural proposals for ADR compliance, conflicts, dependencies, and priorities.
HeadyAICoordinate multi-model governance for high-stakes decisions with cryptographic audit receipts.
abhichandra21Facilitate multi-model technical document review and design debate via GitHub Copilot CLI.
moonlight-lupinDistribute prompts to multiple AI models and log blind comparison votes.
