multi-agent-brainstorming

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

31,392 stars
Complexity: medium

About this skill

This skill orchestrates a formal design review process by employing various specialized AI agents, each assigned a distinct role (e.g., security, scalability, error handling expert). Its primary purpose is to rigorously stress-test and validate proposed designs, such as software architectures or business processes, early in their lifecycle. By enforcing sequential reviews with constrained roles, the skill helps surface hidden assumptions, identify potential failure modes, validate non-functional constraints, and ultimately transform initial ideas into robust, review-validated designs. It is explicitly designed as a sequential design review, not a parallel brainstorming session, preventing 'idea swarm chaos' and ensuring a methodical examination.

Best use case

Designers, architects, or product managers who need a rigorous, automated method to validate new designs, features, or systems before committing to implementation. Ideal for ensuring design robustness, uncovering subtle flaws, and getting diverse critical perspectives without immediate human resource investment.

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

A refined, more robust design that has undergone critical examination. A detailed report outlining identified hidden assumptions, potential failure modes, and vulnerabilities. Specific suggestions for design improvements, clarifications, and considerations for non-functional requirements. Increased confidence in the design's viability and resilience, reducing the likelihood of costly issues post-implementation.

Practical example

Example input

Review this cloud infrastructure design for a new FinTech application. It uses AWS Lambda for serverless functions, Aurora Serverless for the database, and API Gateway for endpoints. Focus on cost optimization, security, and data consistency. Identify any single points of failure and suggest improvements for disaster recovery.

Example output

{"review_summary": "Comprehensive review identified key areas for improvement in cost, security, and disaster recovery.", "agent_feedback": [{"role": "Cost Optimization Agent", "findings": "Proposed Lambda function memory allocation appears excessive for typical workloads; suggest profiling. Aurora Serverless scaling parameters could lead to unexpected costs during idle periods. Consider S3 Intelligent-Tiering for infrequently accessed logs."}, {"role": "Security Agent", "findings": "API Gateway endpoints lack WAF integration for common web exploits. IAM roles for Lambda functions are overly permissive, violating least privilege principle. Data encryption at rest for Aurora is enabled, but transport encryption configuration needs verification."}, {"role": "Data Consistency Agent", "findings": "Potential for eventual consistency issues with cross-region replication if strict ACID properties are required. Consider how concurrent transactions are handled during database scaling events. No clear strategy for orphaned records or failed writes is present."}, {"role": "Resilience Agent", "findings": "Single Region deployment presents a single point of failure. Recommend architectural patterns for active-active or active-passive multi-region setup. Recovery Time Objective (RTO) and Recovery Point Objective (RPO) are not defined; this impacts disaster recovery strategy."}], "design_recommendations": ["Implement stricter IAM policies and WAF rules.", "Define RTO/RPO and plan for multi-region deployment.", "Optimize Lambda and Aurora Serverless configurations for cost."]}

When to use this skill

  • Before the implementation phase of any significant design or system component. When seeking to identify potential vulnerabilities, scalability issues, or logical flaws in a design. To gain structured feedback and anticipate problems early, thereby reducing rework and costly errors down the line. When validating specific non-functional requirements such as security, performance, or resilience.

When not to use this skill

  • When the goal is unstructured, free-form ideation or creative generation (as this is a structured review, not parallel brainstorming). For trivial designs that do not warrant a formal review process. When a design is already in advanced stages of implementation and changes would be prohibitively expensive. When a human-led, collaborative brainstorming session is explicitly required to foster novel ideas.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/multi-agent-brainstorming/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/multi-agent-brainstorming/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/multi-agent-brainstorming/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How multi-agent-brainstorming Compares

Feature / Agentmulti-agent-brainstormingStandard Approach
Platform SupportClaudeLimited / Varies
Context Awareness High Baseline
Installation ComplexitymediumN/A

Frequently Asked Questions

What does this skill do?

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

Which AI agents support this skill?

This skill is designed for Claude.

How difficult is it to install?

The installation complexity is rated as medium. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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
- Offer alternative architectures

Prompting guidance:
> “Assume this design fails in production. Why?”

---

### 3️⃣ Constraint Guardian Agent

**Role:**
- Enforce non-functional and real-world constraints

Focus areas:
- performance
- scalability
- reliability
- security & privacy
- maintainability
- operational cost

**May:**
- Reject designs that violate constraints
- Request clarification of limits

**May NOT:**
- Debate product goals
- Suggest feature changes
- Optimize beyond stated requirements

---

### 4️⃣ User Advocate Agent

**Role:**
- Represent the end user

Focus areas:
- cognitive load
- usability
- clarity of flows
- error handling from user perspective
- mismatch between intent and experience

**May:**
- Identify confusing or misleading aspects
- Flag poor defaults or unclear behavior

**May NOT:**
- Redesign architecture
- Add features
- Override stated user goals

---

### 5️⃣ Integrator / Arbiter Agent

**Role:**
- Resolve conflicts
- Finalize decisions
- Enforce exit criteria

**May:**
- Accept or reject objections
- Require design revisions
- Declare the design complete

**May NOT:**
- Invent new ideas
- Add requirements
- Reopen locked decisions without cause

---

## The Process

### Phase 1 — Single-Agent Design

1. Primary Designer runs the **standard `brainstorming` skill**
2. Understanding Lock is completed and confirmed
3. Initial design is produced
4. Decision Log is started

No other agents participate yet.

---

### Phase 2 — Structured Review Loop

Agents are invoked **one at a time**, in the following order:

1. Skeptic / Challenger
2. Constraint Guardian
3. User Advocate

For each reviewer:
- Feedback must be explicit and scoped
- Objections must reference assumptions or decisions
- No new features may be introduced

Primary Designer must:
- Respond to each objection
- Revise the design if required
- Update the Decision Log

---

### Phase 3 — Integration & Arbitration

The Integrator / Arbiter reviews:
- the final design
- the Decision Log
- unresolved objections

The Arbiter must explicitly decide:
- which objections are accepted
- which are rejected (with rationale)

---

## Decision Log (Mandatory Artifact)

The Decision Log must record:

- Decision made
- Alternatives considered
- Objections raised
- Resolution and rationale

No design is considered valid without a completed log.

---

## Exit Criteria (Hard Stop)

You may exit multi-agent brainstorming **only when all are true**:

- Understanding Lock was completed
- All reviewer agents have been invoked
- All objections are resolved or explicitly rejected
- Decision Log is complete
- Arbiter has declared the design acceptable
- 
If any criterion is unmet:
- Continue review
- Do NOT proceed to implementation
If this skill was invoked by a routing or orchestration layer, you MUST report the final disposition explicitly as one of: APPROVED, REVISE, or REJECT, with a brief rationale.
---

## Failure Modes This Skill Prevents

- Idea swarm chaos
- Hallucinated consensus
- Overconfident single-agent designs
- Hidden assumptions
- Premature implementation
- Endless debate

---

## Key Principles

- One designer, many reviewers
- Creativity is centralized
- Critique is constrained
- Decisions are explicit
- Process must terminate

---

## Final Reminder

This skill exists to answer one question with confidence:

> “If this design fails, did we do everything reasonable to catch it early?”

If the answer is unclear, **do not exit this skill**.

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

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