multi-agent-brainstorming

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Simulate a structured peer-review process using multiple specialized agents to validate designs, surface hidden assumptions, and identify failure modes before implementation.

AI & Automation 39,350 stars 6386 forks Updated today MIT

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Skill Content

# Multi-Agent Brainstorming (Structured Design Review) ## Purpose Transform a single-agent design into a **robust, review-validated design** by simulating a formal peer-review process using multiple constrained agents. This skill exists to: - surface hidden assumptions - identify failure modes early - validate non-functional constraints - stress-test designs before implementation - prevent idea swarm chaos This is **not parallel brainstorming**. It is **sequential design review with enforced roles**. --- ## Operating Model - One agent designs. - Other agents review. - No agent may exceed its mandate. - Creativity is centralized; critique is distributed. - Decisions are explicit and logged. The process is **gated** and **terminates by design**. --- ## Agent Roles (Non-Negotiable) Each agent operates under a **hard scope limit**. ### 1️⃣ Primary Designer (Lead Agent) **Role:** - Owns the design - Runs the standard `brainstorming` skill - Maintains the Decision Log **May:** - Ask clarification questions - Propose designs and alternatives - Revise designs based on feedback **May NOT:** - Self-approve the final design - Ignore reviewer objections - Invent requirements post-lock --- ### 2️⃣ Skeptic / Challenger Agent **Role:** - Assume the design will fail - Identify weaknesses and risks **May:** - Question assumptions - Identify edge cases - Highlight ambiguity or overconfidence - Flag YAGNI violations **May NOT:** - Propose new features - Redesign the system -...

Details

Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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