MCPInstall

debate-workflow

Structured multi-perspective debate for important architectural decisions and complex trade-offs

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/rysweet/amplihack --skill debate-workflow
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads debate-workflow automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Debate Workflow Skill

Purpose

Implement structured multi-perspective debate for important architectural decisions, design trade-offs, and complex problems where multiple valid approaches exist.

When to Use This Skill

USE FOR:

  • Major architectural decisions (framework selection, system design)
  • Complex trade-offs with no clear winner
  • Controversial changes affecting multiple teams
  • High-impact decisions requiring buy-in
  • When perspectives genuinely conflict

AVOID FOR:

  • Simple implementation choices
  • Decisions with obvious correct answer
  • Time-sensitive hot fixes
  • Minor refactoring
  • Routine feature additions

Configuration

Core Parameters

Number of Perspectives:

  • 3 - Default (security, performance, simplicity)
  • 5 - Extended (add: maintainability, user-experience)
  • 7 - Comprehensive (add: scalability, cost)

Debate Rounds:

  • 2 - Quick (position + challenge)
  • 3 - Standard (position + challenge + synthesis)
  • 4-5 - Deep (multiple challenge/response cycles)

Convergence Criteria:

  • 100% - Strong consensus (all perspectives agree)
  • 2/3 - Majority rule (two-thirds agreement)
  • synthesis - Facilitator synthesizes best hybrid
  • evidence - Follow strongest evidence/arguments

Standard Perspective Profiles

Security Perspective:

  • Focus: Vulnerabilities, attack vectors, data protection
  • Questions: "What could go wrong? How do we prevent breaches?"
  • Agent: security agent

Performance Perspective:

  • Focus: Speed, scalability, resource efficiency
  • Questions: "Will this scale? What are the bottlenecks?"
  • Agent: optimizer agent

Simplicity Perspective:

  • Focus: Minimal complexity, ruthless simplification
  • Questions: "Is this the simplest solution? Can we remove abstractions?"
  • Agent: cleanup agent + reviewer agent

Maintainability Perspective:

  • Focus: Long-term evolution, technical debt
  • Questions: "Can future developers understand this? How hard to change?"
  • Agent: reviewer agent + architect agent

User Experience Perspective:

  • Focus: API design, usability, developer experience
  • Questions: "Is this intuitive? How will users interact with this?"
  • Agent: api-designer agent

Scalability Perspective:

  • Focus: Growth capacity, distributed systems
  • Questions: "What happens at 10x load? 100x?"
  • Agent: optimizer agent + architect agent

Cost Perspective:

  • Focus: Resource usage, infrastructure costs, development time
  • Questions: "What's the ROI? Are we over-engineering?"
  • Agent: analyzer agent

Execution Process

Step 1: Frame the Decision

  • Use ambiguity agent to clarify the decision to be made
  • Use prompt-writer agent to create clear decision prompt
  • Define decision scope and constraints
  • Identify stakeholder concerns
  • List evaluation criteria
  • Document explicit user requirements that constrain options
  • CRITICAL: Frame decision as question, not predetermined answer

Decision Framing Template:

# Decision: [Brief Title]

## Question

[One-sentence question to be debated]

## Context

[Why this decision matters, background information]

## Constraints

[Non-negotiable requirements, technical limitations]

## Evaluation Criteria

[How we'll judge proposed solutions]

## Perspectives to Include

[Which viewpoints are most relevant]

Example:

# Decision: Data Storage Strategy for User Analytics

## Question

Should we use PostgreSQL with JSONB, MongoDB, or ClickHouse
for storing and querying user analytics events?

## Context

- 10M events/day expected at launch
- 100M events/day within 2 years
- Complex queries for dashboard analytics
- Real-time and historical reporting needed

## Constraints

- Must handle 10M events/day minimum
- Query latency < 200ms for dashboards
- Budget: $5K/month infrastructure
- Team familiar with PostgreSQL, not ClickHouse

## Evaluation Criteria

1. Performance at scale
2. Query flexibility
3. Operational complexity
4. Cost at scale
5. Team learning curve

## Perspectives to Include

Performance, Cost, Maintainability, Scalability

Step 2: Initialize Perspectives

  • Select N perspectives relevant to decision
  • Spawn Claude subprocess for each perspective
  • Each subprocess receives decision framing doc
  • Each subprocess assigned perspective profile
  • No context sharing between perspectives yet
  • Each forms initial position independently

Initial Position Requirements:

  • State recommended approach
  • Provide 3-5 supporting arguments
  • Identify risks of alternative approaches
  • Quantify claims where possible

Step 3: Debate Round 1 - Initial Positions

  • Collect initial positions from all perspectives
  • Use analyzer agent to synthesize positions
  • Document each perspective's recommendation
  • Identify areas of agreement
  • Identify areas of conflict
  • Surface assumptions made by each perspective

Round 1 Output Structure:

## Security Perspective: [Recommendation]

Arguments For:

1. [Argument with evidence]
2. [Argument with evidence]
3. [Argument with evidence]

Concerns About Alternatives:

- [Alternative A]: [Specific concern]
- [Alternative B]: [Specific concern]

Assumptions:

- [Assumption 1]
- [Assumption 2]

Step 4: Debate Round 2 - Challenge and Respond

  • Share all Round 1 positions with all perspectives
  • Each perspective challenges other perspectives' arguments
  • Each perspective defends their position against challenges
  • Use analyzer agent to track argument strength
  • Identify which arguments withstand scrutiny
  • Document concessions and refinements

Challenge Format:

## [Perspective A] challenges [Perspective B]

Challenge: [Question or counter-argument]
Evidence: [Supporting data or examples]
Request: [What would change your position?]

Response Format:

## [Perspective B] responds to [Perspective A]

Response: [Address the challenge]
Concession: [Points where you agree or adjust]
Counter: [Additional evidence or reasoning]

Step 5: Debate Round 3 - Find Common Ground

  • Identify points of consensus across perspectives
  • Surface remaining disagreements explicitly
  • Explore hybrid approaches combining insights
  • Use architect agent to design synthesis options
  • Validate hybrid approaches against all perspectives
  • Document convergence or divergence

Convergence Analysis:

## Areas of Agreement

1. [Consensus point 1]
2. [Consensus point 2]

## Remaining Disagreements

1. [Disagreement 1]
   - Security says: [position]
   - Performance says: [position]
   - Potential resolution: [hybrid approach]

## Hybrid Approaches Identified

1. [Hybrid Option 1]
   - Combines: [which perspectives]
   - Trade-offs: [explicit costs/benefits]

Step 6: Facilitator Synthesis

  • Use architect agent as neutral facilitator
  • Use analyzer agent to evaluate all arguments
  • Review all debate rounds systematically
  • Identify strongest evidence-based arguments
  • Make recommendation with confidence level
  • Document decision rationale thoroughly
  • Include dissenting views explicitly

Synthesis Structure:

## Facilitator Synthesis

### Recommendation

[Clear statement of recommended approach]

### Confidence Level

[High/Medium/Low] confidence based on:

- Consensus level: [X% of perspectives agree]
- Evidence quality: [Strong/Moderate/Weak]
- Risk level: [Low/Medium/High if wrong]

### Rationale

[Explanation of why this recommendation]

### Key Arguments That Won

1. [Argument that swayed decision]
2. [Argument that swayed decision]
3. [Argument that swayed decision]

### Key Arguments Against (Dissenting Views)

1. [Strongest counter-argument]
2. [Remaining concern]

### Implementation Guidance

[How to execute this decision]

### Success Metrics

[How we'll know if this was the right choice]

### Revisit Triggers

[Conditions that would require reconsidering this decision]

Step 7: Decision Documentation

  • Create decision record: decisions/YYYY-MM-DD-decision-name.md
  • Document full debate transcript
  • Include all perspective arguments
  • Record synthesis and final decision
  • Store in memory using store_discovery() from amplihack.memory.discoveries
  • Update relevant architecture docs

Decision Record Template:

# Decision Record: [Title]

Date: [YYYY-MM-DD]
Status: Accepted
Decision Makers: [List perspectives included]

## Context

[What decision was needed and why]

## Decision

[What was decided]

## Consequences

[What happens because of this decision]

## Alternatives Considered

[What other options were debated]

## Debate Summary

[Key arguments from each perspective]

## Dissenting Opinions

[Perspectives that disagreed and why]

## Review Date

[When to revisit this decision]

---

## Full Debate Transcript

### Round 1: Initial Positions

[Complete positions from all perspectives]

### Round 2: Challenges and Responses

[All challenge/response exchanges]

### Round 3: Convergence Analysis

[Common ground and hybrid approaches]

### Facilitator Synthesis

[Complete synthesis document]

Step 8: Implement Decision

  • Use builder agent to implement chosen approach
  • Follow the decided path from synthesis
  • Implement monitoring for success metrics
  • Set up alerts for revisit triggers
  • Document decision in code comments
  • Create runbook if operational complexity added

Trade-Offs

Cost: Multiple agent cycles, longer decision time Benefit: Well-reasoned decisions, surface hidden risks Best For: Decisions that are expensive to reverse

Examples

Example 1: API Design - REST vs GraphQL

Configuration:

  • Perspectives: 5 (Simplicity, Performance, User-Experience, Maintainability, Cost)
  • Rounds: 3
  • Convergence: Synthesis

Debate Summary:

  • Simplicity: REST is straightforward, well-understood
  • Performance: GraphQL reduces over-fetching, fewer round trips
  • UX: GraphQL gives frontend flexibility, better DX
  • Maintainability: REST easier to version and evolve
  • Cost: GraphQL higher learning curve, more complex infrastructure

Result: REST for initial MVP, GraphQL for v2

  • Rationale: Team knows REST, faster to ship
  • Migration path: Add GraphQL layer in 6 months
  • Trigger: When frontend requests 3+ endpoints per view

Example 2: Testing Strategy - Unit vs Integration Heavy

Configuration:

  • Perspectives: 3 (Simplicity, Maintainability, Performance)
  • Rounds: 2
  • Convergence: 2/3 majority

Debate Summary:

  • Simplicity: Unit tests, mock all dependencies
  • Maintainability: Integration tests, test real interactions
  • Performance: Mix, optimize for feedback speed

Result: 70% unit, 30% integration (Majority agreed)

  • Rationale: Unit tests faster feedback, integration tests catch real issues
  • Dissent: Simplicity wanted 90% unit tests (overruled by maintainability concerns)

Example 3: Deployment Strategy - Kubernetes vs Serverless

Configuration:

  • Perspectives: 5 (Cost, Simplicity, Scalability, Performance, Maintainability)
  • Rounds: 4
  • Convergence: Synthesis (no majority)

Debate Summary:

  • Long, contentious debate with no clear winner
  • Cost and Simplicity favored serverless
  • Scalability and Performance favored Kubernetes
  • Maintainability split (serverless simpler, k8s more control)

Result: Serverless with k8s option researched

  • Rationale: Start simple, team small, serverless faster
  • Hybrid: Evaluate k8s at 10x scale or complex networking needs
  • Strong dissent documented: Performance perspective believes this will need revisiting soon

Philosophy Alignment

This workflow enforces:

  • Perspective Diversity: Multiple viewpoints surface hidden trade-offs
  • Evidence-Based: Arguments must be supported, not just opinions
  • Transparent Trade-offs: Dissent is documented, not hidden
  • Structured Exploration: Debate format prevents premature convergence
  • Decision Quality: Better decisions through rigorous analysis
  • Learning: Debate transcripts become organizational knowledge

Integration with Default Workflow

This workflow replaces Step 4 (Research and Design) of the DEFAULT_WORKFLOW when complex decisions require multi-perspective analysis. Implementation (Step 5) proceeds with the consensus decision.

FAQ

What does the debate-workflow skill do?

Structured multi-perspective debate for important architectural decisions and complex trade-offs

How do I install debate-workflow?

Run npx skills add https://github.com/rysweet/amplihack --skill debate-workflow in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/debate-workflow/ (personal) or .claude/skills/debate-workflow/ (project).

Which AI agents can use debate-workflow?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is debate-workflow safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains debate-workflow?

It is published by rysweet in rysweet/amplihack. It has 239 recorded installs.

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