Consensus Voting Skill

Purpose

Execute weighted multi-agent voting for critical decisions where domain expertise matters. Unlike debate (collaborative synthesis) or n-version (parallel generation), this skill focuses on structured voting with expertise weighting for security, authentication, and data-handling changes.

Key Insight from Pattern Analysis

From ~/.amplihack/.claude/context/DISCOVERIES.md (Pattern Applicability Analysis):

Voting vs Expert Judgment Selection Criteria

When Voting Works:

• Adversarial environment (can't trust individual nodes)
• Binary or simple discrete choices
• No objective quality metric available
• Consensus more valuable than correctness

When Expert Judgment Works:

• Cooperative environment (honest actors)
• Complex quality dimensions
• Objective evaluation criteria exist
• Correctness more valuable than consensus

This skill uses BOTH strategically: Expert judgment to evaluate, weighted voting to decide.

When to Use This Skill

AUTO-TRIGGERS (high-risk domains):

• Security implementations (authentication, authorization)
• Encryption and cryptographic code
• Sensitive data handling (PII, credentials)
• Permission and access control changes
• Critical algorithm implementations

EXPLICIT TRIGGERS:

• Major design decisions with competing approaches
• When stakeholder buy-in matters
• Binary or discrete choices needing validation
• Risk-mitigating decisions for production code

AVOID FOR:

• Complex trade-off analysis (use debate-workflow instead)
• Code generation (use n-version-workflow instead)
• Simple implementation choices
• Subjective quality assessments

Configuration

Voting Configuration

Voting Mode:

• simple-majority - 50%+ votes to pass
• supermajority - 66%+ votes to pass (DEFAULT for security)
• unanimous - 100% agreement required

Agent Selection:

• auto - Select agents based on detected domain (DEFAULT)
• manual - Specify agents explicitly
• comprehensive - All relevant agents vote

Weight Calibration:

• static - Fixed weights per domain (DEFAULT)

Domain Expertise Weights

Agent	Security	Auth	Data	Algorithm	General
security	3.0	2.5	2.5	1.5	1.0
reviewer	1.5	1.5	1.5	2.0	2.0
architect	1.5	2.0	2.0	2.5	2.0
tester	1.0	1.5	1.5	2.0	1.5
optimizer	0.5	0.5	0.5	2.5	1.0
cleanup	0.5	0.5	0.5	1.0	1.5

Weight Interpretation:

• 3.0 = Domain expert (vote counts triple)
• 2.0 = Significant expertise (vote counts double)
• 1.0 = General competence (standard vote)
• 0.5 = Peripheral relevance (half vote)

Execution Process

Step 1: Detect Decision Domain

Analyze the change or decision to determine primary domain:

• Use analyzer agent to examine code/proposal
• Detect keywords: auth, encrypt, password, token, permission, credential
• Identify file paths: auth/, security/, crypto/
• Classify: SECURITY | AUTH | DATA | ALGORITHM | GENERAL

Domain Detection Triggers:

SECURITY: encrypt, decrypt, hash, salt, vulnerability, CVE, injection
AUTH: login, logout, session, token, jwt, oauth, password, credential
DATA: pii, gdpr, sensitive, personal, private, secret, key
ALGORITHM: sort, search, calculate, compute, process, transform

Step 2: Select Voting Agents

Based on detected domain, select relevant agents with weights:

For SECURITY domain:

• security agent (weight: 3.0) - Primary expert
• architect agent (weight: 1.5) - System design perspective
• reviewer agent (weight: 1.5) - Code quality check
• tester agent (weight: 1.0) - Testability assessment

For AUTH domain:

• security agent (weight: 2.5) - Security implications
• architect agent (weight: 2.0) - Integration patterns
• reviewer agent (weight: 1.5) - Implementation quality
• tester agent (weight: 1.5) - Auth flow testing

For DATA domain:

• security agent (weight: 2.5) - Data protection
• architect agent (weight: 2.0) - Data architecture
• reviewer agent (weight: 1.5) - Handling patterns
• tester agent (weight: 1.5) - Data validation

For ALGORITHM domain:

• optimizer agent (weight: 2.5) - Performance analysis
• architect agent (weight: 2.5) - Design patterns
• tester agent (weight: 2.0) - Correctness testing
• reviewer agent (weight: 2.0) - Code quality

Step 3: Present Decision to Agents

Each selected agent receives:

• Clear decision statement
• Available options (if applicable)
• Relevant context and constraints
• Evaluation criteria

Decision Prompt Template:

## Decision Required: [TITLE]

**Domain:** [SECURITY | AUTH | DATA | ALGORITHM]
**Your Weight:** [X.X] (based on domain expertise)

### Context

[Relevant background and constraints]

### Options

1. [Option A]: [Description]
2. [Option B]: [Description]
3. [Reject Both]: Propose alternative

### Evaluation Criteria

- Security implications
- Implementation complexity
- Maintainability
- Risk assessment

### Your Vote

Provide:

1. Your vote (Option 1, 2, or Reject)
2. Confidence level (HIGH, MEDIUM, LOW)
3. Key reasoning (2-3 sentences max)
4. Any conditions or caveats

Step 4: Collect Votes

For each agent, collect structured vote:

agent: security
weight: 3.0
vote: Option 1
confidence: HIGH
reasoning: "Option 1 follows OWASP best practices for credential storage. Option 2 uses deprecated hashing algorithm."
conditions: ["Ensure salt length >= 16 bytes", "Use constant-time comparison"]

Step 5: Calculate Weighted Result

Weighted Vote Calculation:

For each option:
  weighted_score = sum(agent_weight * confidence_multiplier)

Where confidence_multiplier:
  HIGH = 1.0
  MEDIUM = 0.7
  LOW = 0.4

Example Calculation:

Agent	Weight	Vote	Confidence	Score
security	3.0	A	HIGH	3.0
architect	1.5	A	MEDIUM	1.05
reviewer	1.5	B	HIGH	1.5
tester	1.0	A	LOW	0.4

Option A Score: 3.0 + 1.05 + 0.4 = 4.45
Option B Score: 1.5
Total Weighted Votes: 5.95
Option A Percentage: 74.8% (SUPERMAJORITY)

Step 6: Apply Voting Threshold

Based on configured voting mode:

simple-majority (50%+):

• Option with highest weighted score wins if > 50%
• If no option > 50%, proceed to debate or reject

supermajority (66%+): (DEFAULT for security)

• Winning option must have > 66% weighted votes
• Provides stronger validation for high-risk decisions

unanimous (100%):

• All agents must agree (rare, highest bar)
• Any dissent blocks decision

Step 7: Report Consensus Result

If Consensus Reached:

## Consensus Voting Result

**Decision:** [Selected Option]
**Domain:** SECURITY
**Threshold:** Supermajority (66%+)
**Result:** PASSED (74.8%)

### Vote Summary

| Agent     | Weight | Vote | Confidence |
| --------- | ------ | ---- | ---------- |
| security  | 3.0    | A    | HIGH       |
| architect | 1.5    | A    | MEDIUM     |
| reviewer  | 1.5    | B    | HIGH       |
| tester    | 1.0    | A    | LOW        |

### Key Reasoning (from highest-weighted agents)

- **security (3.0):** "Option 1 follows OWASP best practices..."

### Conditions/Caveats

- Ensure salt length >= 16 bytes
- Use constant-time comparison

### Dissenting View

- **reviewer (1.5):** "Option B has simpler implementation..."

If No Consensus:

## Consensus Voting Result

**Decision:** NO CONSENSUS
**Domain:** AUTH
**Threshold:** Supermajority (66%+)
**Result:** FAILED (52.3%)

### Recommendation

- Escalate to `/amplihack:debate` for structured trade-off analysis
- OR: Gather more information and re-vote
- OR: Accept simple majority with documented risk

Step 8: Record Decision

• Log voting result to session decisions
• Document vote reasoning for future reference

Trade-Offs

Benefits:

• Structured decision-making for high-risk domains
• Domain expertise appropriately weighted
• Clear audit trail with reasoning
• Faster than full debate for binary/discrete choices

Costs:

• Less nuanced than debate (no synthesis)
• Requires clear options (not generative)
• Weight calibration needs real data
• May miss creative alternatives

Use When: Decision is discrete, domain expertise matters, audit trail needed

Examples

Example 1: Password Hashing Implementation

Context: Choosing between bcrypt, argon2id, and PBKDF2 for new auth system

Domain Detection: AUTH (password, hash)

Agents Selected:

• security (2.5), architect (2.0), reviewer (1.5), tester (1.5)

Votes:

• security: argon2id (HIGH) - "OWASP current recommendation, memory-hard"
• architect: argon2id (MEDIUM) - "Good library support, modern design"
• reviewer: bcrypt (HIGH) - "More battle-tested in production"
• tester: argon2id (MEDIUM) - "Easier to test with configurable params"

Result: argon2id wins with 68.2% (supermajority passed)

Example 2: API Rate Limiting Approach

Context: Token bucket vs sliding window vs fixed window

Domain Detection: SECURITY (rate limit, protection)

Agents Selected:

• security (3.0), optimizer (1.5), architect (1.5), reviewer (1.5)

Votes:

• security: token bucket (HIGH) - "Best protection against burst attacks"
• optimizer: sliding window (MEDIUM) - "Better resource utilization"
• architect: token bucket (MEDIUM) - "Industry standard, well-understood"
• reviewer: sliding window (LOW) - "Simpler implementation"

Result: token bucket wins with 69.4% (supermajority passed)

Example 3: No Consensus Scenario

Context: Microservices vs monolith for new feature

Domain Detection: ALGORITHM (general architecture)

Agents Selected:

• architect (2.5), optimizer (2.5), reviewer (2.0), tester (2.0)

Votes:

• architect: microservices (MEDIUM) - "Better long-term scalability"
• optimizer: monolith (HIGH) - "Simpler operations, less overhead"
• reviewer: monolith (MEDIUM) - "Easier to maintain initially"
• tester: microservices (LOW) - "Harder to test but more isolated"

Result: No consensus (52.1%) - Escalate to /amplihack:debate

Integration with Other Workflows

Handoff to Debate

When voting fails to reach consensus:

1. Document vote results and reasoning
2. Invoke Skill(debate-workflow) with vote context
3. Use voting insights to frame debate perspectives

After N-Version Implementation

Use consensus voting to select between N-version implementations:

1. N-version generates 3+ implementations
2. Consensus voting selects winner
3. Domain experts have weighted influence on selection

Philosophy Alignment

This skill enforces:

• Evidence-Based Decisions: Votes require reasoning
• Domain Expertise: Weights reflect competence
• Transparent Trade-offs: Dissent documented
• Audit Trail: Full voting record preserved
• Appropriate Rigor: Auto-triggers for high-risk domains