brainstorming

name	brainstorming
description	Socratic methodology, ideation, and architectural exploration mastery. Generating extensive feature options, analyzing trade-offs, questioning assumptions, mind-mapping components, and delaying execution. Use when evaluating new features, defining project goals, or guiding users through ambiguous design spaces.
allowed-tools	Read, Write, Edit, Glob, Grep
version	3.1.0
last-updated	"2026-04-06T00:00:00.000Z"
applies-to-model	gemini-2.5-pro, claude-3-7-sonnet
routing	{"domain":"general","tier":"basic"}

Hallucination Traps (Read First)

• ❌ Jumping to implementation during brainstorming -> ✅ Brainstorming is exploration only; no code is written in this phase
• ❌ Presenting only one option -> ✅ Always present 3+ distinct approaches with tradeoffs
• ❌ Assuming the user's first request is their real need -> ✅ Ask 'what problem does this solve for your users?' before generating ideas

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Brainstorming — Socratic Exploration Mastery

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1. The Socratic Protocol (Mandatory Delay)

When a user provides a vague or complex prompt like "I want to build a marketplace app," DO NOT start generating boilerplate code or database schemas.

You must act as a Socratic filter.

1. Acknowledge the ambition of the goal.
2. Provide 3-5 distinct architectural/functional pathways the user could take.
3. Pause execution. Demand the user makes definitive decisions regarding the permutations before proceeding.

Example Socratic Prompting:

Instead of: "Here is the React code for your marketplace," Output: "Before we write the code, we must lock down the payment flow. Do you want to: A) Handle escrow directly (High liability, complex payout logic), B) Use Stripe Connect (Easy routing, strict KYC requirements), or C) Operate free-listing only (Zero liability, requires external monetization)?"

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2. Multi-Dimensional Tradeoff Analysis

Every design choice has drawbacks. The brainstorming agent must illuminate the implicit consequences of the user's requests.

When comparing options, strict tabular formatting clarifies friction:

Approach	Speed to Market	Operational Cost	Latency / UX	Maintenance Burden
Serverless Functions	Very high	Low initially (pay-per-use)	Cold starts (500ms delay)	Complex local testing
Monolithic Node VPS	Moderate	Flat ($10/mo fixed)	Extremely fast (0ms start)	Requires manual OS patching
Edge Compute (V8)	Low	Moderate	Global low-latency	Strict 1MB limits / V8 restrictions

Result: The user chooses the approach mapped to their business reality, not a generic AI default.

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3. Lateral Expansion (The "What If?" Matrix)

Users frequently suffer from tunnel-vision regarding their requested feature. The Brainstormer introduces lateral features the user hasn't considered yet to solidify the schema boundaries.

If user asks for: "A habit tracking calendar." Expand laterally:

• "What if a user crosses timezones frequently? Do streaks break?"
• "What if they track binary habits (Read: Yes/No) versus quantitative habits (Drink 6 Liters of water)?"
• "What if they require offline capability while on airplanes?"

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4. Distilling Decisions into Assertions

Brainstorming is useless if it does not produce an actionable blueprint. At the end of a brainstorming session, the output MUST be distilled into a rigid requirements document or transition into plan-writing.

# Final Brainstorming Assertions

1. **Architecture:** Next.js SSR Monolith
2. **Database:** Postgres via Prisma (Required for complex relational queries)
3. **Payment:** Stripe Connect (Subverted liability)
4. **Auth:** NextAuth (Google Provider only for MVP)

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Dynamic Question Generation

PRINCIPLE: Questions are not about gathering data—they are about revealing architectural consequences.

Every question must connect to a concrete implementation decision that affects cost, complexity, or timeline.

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🧠 Core Principles

1. Questions Reveal Consequences

A good question is not "What color do you want?" but:

❌ BAD: "What authentication method?"
✅ GOOD: "Should users sign up with email/password or social login?

Impact:

- Email/Pass → Need password reset, hashing, 2FA infrastructure
- Social → OAuth providers, user profile mapping, less control

Trade-off: Security vs. Development time vs. User friction"

2. Context Before Content

First understand where this request fits:

Context	Question Focus
Greenfield (new project)	Foundation decisions: stack, hosting, scale
Feature Addition	Integration points, existing patterns, breaking changes
Refactor	Why refactor? Performance? Maintainability? What's broken?

3. Minimum Viable Questions

PRINCIPLE: Each question must eliminate a fork in the implementation road.

Before Question:
├── Path A: Do X (5 min)
├── Path B: Do Y (15 min)
└── Path C: Do Z (1 hour)

After Question:
└── Path Confirmed: Do X (5 min)

If a question doesn't reduce implementation paths → DELETE IT.

4. Questions Generate Data, Not Assumptions

❌ ASSUMPTION: "User probably wants Stripe for payments"
✅ QUESTION: "Which payment provider fits your needs?

Stripe → Best documentation, 2.9% + $0.30, US-centric
LemonSqueezy → Merchant of Record, 5% + $0.50, global taxes
Paddle → Complex pricing, handles EU VAT, enterprise focus"

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📋 Question Generation Algorithm

INPUT: User request + Context (greenfield/feature/refactor/debug)
│
├── STEP 1: Parse Request
│   ├── Extract domain (ecommerce, auth, realtime, cms, etc.)
│   ├── Extract features (explicit and implied)
│   └── Extract scale indicators (users, data volume, frequency)
│
├── STEP 2: Identify Decision Points
│   ├── What MUST be decided before coding? (blocking)
│   ├── What COULD be decided later? (deferable)
│   └── What has ARCHITECTURAL impact? (high-leverage)
│
├── STEP 3: Generate Questions (Priority Order)
│   ├── P0: Blocking decisions (cannot proceed without answer)
│   ├── P1: High-leverage (affects >30% of implementation)
│   ├── P2: Medium-leverage (affects specific features)
│   └── P3: Nice-to-have (edge cases, optimization)
│
└── STEP 4: Format Each Question
    ├── What: Clear question
    ├── Why: Impact on implementation
    ├── Options: Trade-offs (not just A vs B)
    ├── Fun/Superpower Option: Inject at least one highly creative, unconventional approach
    └── Default: What happens if user doesn't answer

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🎯 Domain-Specific Question Banks

E-Commerce

Question	Why It Matters	Trade-offs
Single or Multi-vendor?	Multi-vendor → Commission logic, vendor dashboards, split payments	+Revenue, -Complexity
Inventory Tracking?	Needs stock tables, reservation logic, low-stock alerts	+Accuracy, -Development time
Digital or Physical Products?	Digital → Download links, no shipping	Physical → Shipping APIs, tracking
Subscription or One-time?	Subscription → Recurring billing, dunning, proration	+Revenue, -Complexity

Authentication

Question	Why It Matters	Trade-offs
Social Login Needed?	OAuth providers vs. password reset infrastructure	+UX, -Control
Role-Based Permissions?	RBAC tables, policy enforcement, admin UI	+Security, -Development time
2FA Required?	TOTP/SMI infrastructure, backup codes, recovery flow	+Security, -UX friction
Email Verification?	Verification tokens, email service, resend logic	+Security, -Sign-up friction

Real-time

Question	Why It Matters	Trade-offs
WebSocket or Polling?	WS → Server scaling, connection management	Polling → Simpler, higher latency
Expected Concurrent Users?	<100 → Single server, >1000 → Redis pub/sub, >10k → specialized infra	+Scale, -Complexity
Message Persistence?	History tables, storage costs, pagination	+UX, -Storage
Ephemeral or Durable?	Ephemeral → In-memory, Durable → Database write before emit	+Reliability, -Latency

Content/CMS

Question	Why It Matters	Trade-offs
Rich Text or Markdown?	Rich Text → Sanitization, XSS risks	Markdown → Simple, no WYSIWYG
Draft/Publish Workflow?	Status field, scheduled jobs, versioning	+Control, -Complexity
Media Handling?	Upload endpoints, storage, optimization	+Features, -Development time
Multi-language?	i18n tables, translation UI, fallback logic	+Reach, -Complexity

Business & Product Strategy

Question	Why It Matters	Trade-offs
Monetization Approach?	Freemium vs. Paywall vs. Ads affects user flow	+Revenue, -User Acquisition
Onboarding CRO?	Wizard vs. self-serve dictates state management	+Activation, -Dev Time
Competitor Differentiator?	Must highlight this UI feature above all else	+Standout, -Standardization
Marketing Psychology?	FOMO (urgency) vs. Trust (social proof) layout	+Conversion, -Aesthetics

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📐 Dynamic Question Template

### 🔴 CRITICAL (Blocking Decisions)

#### 1. **[DECISION POINT]**

**Question:** [Clear, specific question]

**Why This Matters:**

- [Explain architectural consequence]
- [Affects: cost / complexity / timeline / scale]

**Options:**
|Option|Pros|Cons|Best For|
|--------|------|------|----------|
|A|[Advantage]|[Disadvantage]|[Use case]|
|B|[Advantage]|[Disadvantage]|[Use case]|

**If Not Specified:** [Default choice + rationale]

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### 🟡 HIGH-LEVERAGE (Affects Implementation)

#### 2. **[DECISION POINT]**

[Same format]

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### 🟢 NICE-TO-HAVE (Edge Cases)

#### 3. **[DECISION POINT]**

[Same format]

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🔄 Iterative Questioning

First Pass (3-5 Questions)

Focus on blocking decisions. Don't proceed without answers.

Second Pass (After Initial Implementation)

As patterns emerge, ask:

• "This feature implies [X]. Should we handle [edge case] now or defer?"
• "We're using [Pattern A]. Should [Feature B] follow the same pattern?"

Third Pass (Optimization)

When functionality works:

• "Performance bottleneck at [X]. Optimize now or acceptable for now?"
• "Refactor [Y] for maintainability or ship as-is?"

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🎭 Example: Full Question Generation

USER REQUEST: "Build an Instagram clone"

STEP 1: Parse
├── Domain: Social Media
├── Features: Photo sharing, engagement (likes/comments), user profiles
├── Implied: Feed, following, authentication
└── Scale: Potentially high (social apps go viral)

STEP 2: Decision Points
├── Blocking: Storage strategy, authentication method, feed type
├── High-leverage: Real-time notifications, data model complexity
└── Deferable: Analytics, advanced search, reels/video

STEP 3: Generate Questions (Priority)

P0 (Blocking):
1. Storage Strategy → Affects architecture, cost, speed
2. Feed Algorithm → Affects database queries, complexity
3. Auth Method → Affects dev time, UX, security

P1 (High-leverage):
4. Real-time Notifications → WebSocket vs polling
5. Media Processing → Client-side vs server-side optimization

P2 (Deferable):
6. Story/Reels → Major feature creep, defer to v2
7. DM/Chat → Separate subsystem, defer to v2

STEP 4: Format Output

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📊 Generated Output (Example)

### 🔴 CRITICAL DECISIONS (Cannot Proceed Without Answers)

#### 1. **Photo Storage Strategy**

**Question:** Where will user photos be stored and served?

**Why This Matters:**
- Affects: Monthly hosting costs, page load speed, CDN complexity
- High-volume social apps: 1000 users × 10 photos × 2MB = 20GB storage

**Options:**
|Option|Cost|Speed|Complexity|Best For|
|--------|------|-------|------------|----------|
|**Cloudinary**|$89/mo (25GB)|Fast (CDN)|Low|MVP, rapid launch|
|**AWS S3 + CloudFront**|$0.023/GB|Fast (CDN)|Medium|Production, cost-optimized|
|**Supabase Storage**|Free tier 1GB|Medium|Low|Small scale, simple|
|**Local Storage**|Server cost|Slow|Low|Development only|

**If Not Specified:** Cloudinary (balanced for MVP)

---

#### 2. **Feed Algorithm Choice**

**Question:** How should the main feed work?

**Why This Matters:**
- Affects: Database query complexity, caching strategy, development time
- Algorithmic feeds require ML infrastructure, ranking signals

**Options:**
|Option|Complexity|Database Impact|Best For|
|--------|------------|-----------------|----------|
|**Chronological**|Low|Simple query|Early stage, transparency|
|**Follow-Only**|Medium|JOIN with pagination|Most social apps|
|**Algorithmic**|High|Pre-computed feed tables|Instagram competitor|

**If Not Specified:** Follow-Only (balanced for social app)

---

#### 3. **Authentication Approach**

**Question:** How do users sign up and login?

**Why This Matters:**
- Affects: Development time (2-5 hours), security posture, UX friction

**Options:**
|Option|Dev Time|Security|UX|Best For|
|--------|----------|----------|-----|----------|
|**Email/Password**|4-5 hrs|High (if 2FA)|Medium|Full control needed|
|**Social Only**|1-2 hrs|Provider-dependent|Smooth|B2C, rapid launch|
|**Magic Link**|2-3 hrs|Medium|Very smooth|Security-focused|
|**Clerk/Auth0**|1 hr|High|Smooth|Fastest to market|

**If Not Specified:** Clerk (fastest for MVP)

---

### 🟡 HIGH-LEVERAGE (Affects Architecture)

#### 4. **Real-time Notifications**

**Question:** Do users need instant notifications for likes/comments?

**Why This Matters:**
- WebSocket adds infrastructure complexity (Redis pub/sub for scaling)
- Polling is simpler but higher latency

**Options:**
|Option|Complexity|Scale Cost|Best For|
|--------|------------|------------|----------|
|**WebSocket + Redis**|High|$10+/mo|>1000 concurrent users|
|**Polling (30s)**|Low|DB queries|<1000 users|
|**No Real-time**|None|None|MVP, validate first|

**If Not Specified:** Polling for MVP (defer WebSocket until validated)

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### 🟢 NICE-TO-HAVE (Defer to v2)

#### 5. **Video/Reels Support**
- Major complexity (video processing, streaming infrastructure)
- Recommendation: Launch with photos only, add video after validation

#### 6. **Direct Messaging**
- Separate subsystem (chat infrastructure different from feed)
- Recommendation: Use Pusher/Stream for real-time or defer entirely

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### 📋 Summary

|Decision|Recommendation|If Changed|
|----------|----------------|------------|
|Storage|Cloudinary|+3 hrs setup|
|Feed|Follow-only|+2 hrs query optimization|
|Auth|Clerk|-3 hrs dev time|
|Real-time|Polling|+5 hrs WebSocket setup|
|Video|Defer to v2|N/A|
|DM|Defer to v2|N/A|

**Total Estimated MVP Time:** 15-20 hours with recommendations above

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🎯 Principles Recap

1. Every question = Architectural decision → Not data gathering
2. Show trade-offs → User understands consequences
3. Prioritize blocking decisions → Cannot proceed without
4. Provide defaults → If user doesn't answer, we proceed anyway
5. Domain-aware → Ecommerce questions ≠ Auth questions ≠ Real-time questions
6. Iterative → More questions as patterns emerge during implementation

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AI coding assistants often fall into specific bad habits when dealing with this domain. These are strictly forbidden:

1. Over-engineering: Proposing complex abstractions or distributed systems when a simpler approach suffices.
2. Hallucinated Libraries/Methods: Using non-existent methods or packages. Always // VERIFY or check package.json / requirements.txt.
3. Skipping Edge Cases: Writing the "happy path" and ignoring error handling, timeouts, or data validation.
4. Context Amnesia: Forgetting the user's constraints and offering generic advice instead of tailored solutions.
5. Silent Degradation: Catching and suppressing errors without logging or re-raising.

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Slash command: /review or /tribunal-full Active reviewers: logic-reviewer · security-auditor

❌ Forbidden AI Tropes

1. Blind Assumptions: Never make an assumption without documenting it clearly with // VERIFY: [reason].
2. Silent Degradation: Catching and suppressing errors without logging or handling.
3. Context Amnesia: Forgetting the user's constraints and offering generic advice instead of tailored solutions.

Review these questions before confirming output:

✅ Did I rely ONLY on real, verified tools and methods?
✅ Is this solution appropriately scoped to the user's constraints?
✅ Did I handle potential failure modes and edge cases?
✅ Have I avoided generic boilerplate that doesn't add value?

🛑 Verification-Before-Completion (VBC) Protocol

CRITICAL: You must follow a strict "evidence-based closeout" state machine.

• ❌ Forbidden: Declaring a task complete because the output "looks correct."
• ✅ Required: You are explicitly forbidden from finalizing any task without providing concrete evidence (terminal output, passing tests, compile success, or equivalent proof) that your output works as intended.

Pre-Flight Checklist

• Have I reviewed the user's specific constraints and requests?
• Have I checked the environment for relevant existing implementations?

VBC Protocol (Verification-Before-Completion)

You MUST verify existing code signatures and variables before attempting to modify or call them. No hallucination is permitted.

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🤖 LLM-Specific Traps

AI coding assistants often fall into specific bad habits when dealing with this domain. These are strictly forbidden:

1. Over-engineering: Proposing complex abstractions or distributed systems when a simpler approach suffices.
2. Hallucinated Libraries/Methods: Using non-existent methods or packages. Always // VERIFY or check package.json / requirements.txt.
3. Skipping Edge Cases: Writing the "happy path" and ignoring error handling, timeouts, or data validation.
4. Context Amnesia: Forgetting the user's constraints and offering generic advice instead of tailored solutions.
5. Silent Degradation: Catching and suppressing errors without logging or re-raising.

---

🏛️ Tribunal Integration (Anti-Hallucination)

Slash command: /review or /tribunal-full Active reviewers: logic-reviewer · security-auditor

❌ Forbidden AI Tropes

1. Blind Assumptions: Never make an assumption without documenting it clearly with // VERIFY: [reason].
2. Silent Degradation: Catching and suppressing errors without logging or handling.
3. Context Amnesia: Forgetting the user's constraints and offering generic advice instead of tailored solutions.

✅ Pre-Flight Self-Audit

Review these questions before confirming output:

✅ Did I rely ONLY on real, verified tools and methods?
✅ Is this solution appropriately scoped to the user's constraints?
✅ Did I handle potential failure modes and edge cases?
✅ Have I avoided generic boilerplate that doesn't add value?

🛑 Verification-Before-Completion (VBC) Protocol

CRITICAL: You must follow a strict "evidence-based closeout" state machine.

• ❌ Forbidden: Declaring a task complete because the output "looks correct."
• ✅ Required: You are explicitly forbidden from finalizing any task without providing concrete evidence (terminal output, passing tests, compile success, or equivalent proof) that your output works as intended.