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.

23 stars

Best use case

multi-agent-brainstorming is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using multi-agent-brainstorming should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/multi-agent-brainstorming/SKILL.md --create-dirs "https://raw.githubusercontent.com/christophacham/agent-skills-library/main/skills/ai-ml/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 SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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.

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.

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.

Related Skills

performance-testing-review-multi-agent-review

23
from christophacham/agent-skills-library

Use when working with performance testing review multi agent review

sadd:multi-agent-patterns

23
from christophacham/agent-skills-library

Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.

error-debugging-multi-agent-review

23
from christophacham/agent-skills-library

Use when working with error debugging multi agent review

agent-orchestration-multi-agent-optimize

23
from christophacham/agent-skills-library

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

genderapi-io-automation

23
from christophacham/agent-skills-library

Automate Genderapi IO tasks via Rube MCP (Composio). Always search tools first for current schemas.

gender-api-automation

23
from christophacham/agent-skills-library

Automate Gender API tasks via Rube MCP (Composio). Always search tools first for current schemas.

fred-economic-data

23
from christophacham/agent-skills-library

Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.

fidel-api-automation

23
from christophacham/agent-skills-library

Automate Fidel API tasks via Rube MCP (Composio). Always search tools first for current schemas.

fastapi-templates

23
from christophacham/agent-skills-library

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

fastapi-router-py

23
from christophacham/agent-skills-library

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or add...

fastapi-pro

23
from christophacham/agent-skills-library

Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns.

expo-api-routes

23
from christophacham/agent-skills-library

Guidelines for creating API routes in Expo Router with EAS Hosting