multi-agent-brainstorming

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

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$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill multi-agent-brainstorming
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summary

Structured peer-review process using constrained agents to validate designs and surface hidden assumptions before implementation.

  • Five specialized agent roles with hard scope limits: Primary Designer, Skeptic/Challenger, Constraint Guardian, User Advocate, and Integrator/Arbiter, each with explicit permissions and restrictions
  • Sequential review workflow where the designer proposes, then three reviewer agents provide feedback in order, with mandatory Decision Log recording all objections
skill.md

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.

how to use multi-agent-brainstorming

How to use multi-agent-brainstorming on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add multi-agent-brainstorming
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill multi-agent-brainstorming

The skills CLI fetches multi-agent-brainstorming from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/multi-agent-brainstorming

Reload or restart Cursor to activate multi-agent-brainstorming. Access the skill through slash commands (e.g., /multi-agent-brainstorming) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.740 reviews
  • Henry Robinson· Dec 28, 2024

    multi-agent-brainstorming fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Henry Gonzalez· Dec 24, 2024

    multi-agent-brainstorming reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Charlotte Smith· Dec 16, 2024

    multi-agent-brainstorming is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Dec 4, 2024

    We added multi-agent-brainstorming from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Nov 23, 2024

    multi-agent-brainstorming fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Henry Verma· Nov 19, 2024

    We added multi-agent-brainstorming from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Anaya Gupta· Nov 15, 2024

    multi-agent-brainstorming has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Min Khan· Nov 7, 2024

    Solid pick for teams standardizing on skills: multi-agent-brainstorming is focused, and the summary matches what you get after install.

  • Jin Martinez· Nov 3, 2024

    Keeps context tight: multi-agent-brainstorming is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Min Diallo· Oct 26, 2024

    We added multi-agent-brainstorming from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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