Best use case
multi-agent-brainstorming is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/multi-agent-brainstorming/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How multi-agent-brainstorming Compares
| Feature / Agent | multi-agent-brainstorming | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides specific capabilities for your AI agent. See the About section for full details.
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
simo-multiomics-integration-agent
AI-powered spatial integration of multi-omics datasets using probabilistic alignment for comprehensive tissue atlas construction and cellular state mapping.
multi-model-reviewer
協調多個 AI 模型(ChatGPT、Gemini、Codex、QWEN、Claude)進行三角驗證,確保「Specification == Program == Test」一致性。過濾假警報後輸出報告,大幅減少人工介入時間。
multi-ai-research
Comprehensive research and analysis using Claude (subagents), Gemini CLI, and Codex CLI. Multi-perspective research with cross-verification, iterative refinement, and 100% citation coverage. Use for security analysis, architecture research, code quality assessment, performance analysis, or any research requiring rigorous verification and multiple AI perspectives.
multi-ai
Start the multi-AI pipeline with a given request. Guides through plan -> review -> implement -> review workflow.
multi-agent-patterns
Supervisor, swarm, and hierarchical multi-agent architectures with context isolation patterns.
multi-agent-estimation
Build multi-agent AI systems for construction estimation. Use CrewAI/LangGraph to orchestrate specialized agents: QTO agent, pricing agent, validation agent. Automate complex estimation workflows.
error-debugging-multi-agent-review
Use when working with error debugging multi agent review
agent-multi-repo-swarm
Agent skill for multi-repo-swarm - invoke with $agent-multi-repo-swarm
multimodal-doc-converter
Parse and convert multimodal documents (PDF, DOCX, etc.) into structured Markdown with minimal information loss. Use this skill when users need to: (1) convert documents containing text, images, and audio into Markdown format, (2) extract and OCR text from embedded images, (3) recognize and render mathematical formulas, (4) transcribe embedded audio files, (5) preserve document structure and reading order during conversion. Trigger on requests like "convert this PDF to markdown", "extract content from this document", "turn this docx into markdown with OCR".
ai-multimodal
Process and generate multimedia content using Google Gemini API for better vision capabilities. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (better image analysis than Claude models, captioning, reasoning, object detection, design extraction, OCR, visual Q&A, segmentation, handle multiple images), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image with Imagen 4, editing, composition, refinement), generate videos (text-to-video with Veo 3, 8-second clips with native audio). Use when working with audio/video files, analyzing images or screenshots (instead of default vision capabilities of Claude, only fallback to Claude's vision capabilities if needed), processing PDF documents, extracting structured data from media, creating images/videos from text prompts, or implementing multimodal AI features. Supports Gemini 3/2.5, Imagen 4, and Veo 3 models with context windows up to 2M tokens.
u08983-ethical-dilemma-navigation-for-multilingual-translation-services
Operate the "Ethical Dilemma Navigation for multilingual translation services" capability in production for multilingual translation services workflows. Use when mission execution explicitly requires this capability and outcomes must be reproducible, policy-gated, and handoff-ready.
multipar-cli
Comprehensive guide for MultiPar CLI - PAR2 recovery file creation and verification tool. Use when creating redundancy archives, verifying file integrity, or repairing corrupted files. Triggers on: multipar, par2, recovery files, file verification, par2j, parity archive, data recovery, file repair.