debate-workflow

rysweet/amplihack · updated Apr 8, 2026

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$npx skills add https://github.com/rysweet/amplihack --skill debate-workflow
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summary

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

skill.md

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+ endpoint
how to use debate-workflow

How to use debate-workflow 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 debate-workflow
2

Execute installation command

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

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

The skills CLI fetches debate-workflow from GitHub repository rysweet/amplihack 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/debate-workflow

Reload or restart Cursor to activate debate-workflow. Access the skill through slash commands (e.g., /debate-workflow) 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.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

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

Ratings

4.549 reviews
  • Hana Shah· Dec 28, 2024

    We added debate-workflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ama Agarwal· Dec 20, 2024

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

  • Sofia Sethi· Dec 20, 2024

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

  • Hiroshi Okafor· Nov 19, 2024

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

  • Ava Park· Nov 11, 2024

    We added debate-workflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ava Haddad· Nov 7, 2024

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

  • Charlotte Menon· Oct 26, 2024

    debate-workflow has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hiroshi Sanchez· Oct 10, 2024

    Registry listing for debate-workflow matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Carlos Khanna· Oct 2, 2024

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

  • Arya Gonzalez· Sep 17, 2024

    We added debate-workflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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