multi-agent-brainstorming▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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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
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
brainstormingskill - 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
- Primary Designer runs the standard
brainstormingskill - Understanding Lock is completed and confirmed
- Initial design is produced
- 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:
- Skeptic / Challenger
- Constraint Guardian
- 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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches multi-agent-brainstorming from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★40 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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