Coordinate multi-perspective design reviews and produce revised documents with actionable findings.
npx skills add https://github.com/dnhess/spectra --skill deep-design
Please help me install this Agent Skill. Skill: deep-design Source: https://github.com/dnhess/spectra/tree/main/deep-design Command: npx skills add https://github.com/dnhess/spectra --skill deep-design
Orchestrates a structured, multi-agent design review to stress-test documents, specs, or ideas from every angle and produce a revised document that incorporates all findings as actionable conclusions.
Prompt the system to run a deep-design review on the provided document and return a consolidated revised document with findings.
High-intent search queries and answers about installing and using this skill.
Run a multi-perspective design review by prompting the system to evaluate your architecture spec, which coordinates diverse agents to stress-test the document and outputs a revised version with actionable findings.
A blackboard architecture for design revision is a shared session where multiple agents write critique outputs to a centralized log, ensuring traceable rationale and convergent discussion for finalized document updates.
Yes, you can use a multi-agent review for product requirements documents to enforce structured critique, trace each finding to source context, and produce a revised PRD with actionable recommendations before implementation.
The best way to stress-test a feature proposal from multiple angles is applying a coordinated, multi-agent critique that surfaces design flaws and improvement opportunities, synthesizing outputs into a consolidated revised document.
No, a structured multi-perspective design review requires no specific dependencies or components, operating independently to coordinate agent prompts and generate finalized revised documents with traceable rationale.
You should not use an automated multi-agent design review for simple documents not intended for implementation, as the structured critique and blackboard collaboration overhead provides most value for complex architecture designs and feature proposals.
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