adversarial-debate

name	adversarial-debate
description	N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
version	1.0.0
model	sonnet
invoked_by	both
user_invocable	true
tools	["Read","Write","Edit","Bash","Glob","Grep","Skill","TaskUpdate","TaskCreate","TaskGet"]
agents	["architect","planner","security-architect","general-assistant"]
category	Specialized Patterns
tags	["debate","trade-off","analysis","decision","architecture","pro-con","adversarial","reasoning"]
best_practices	["Assign clear opposing stances before debate begins — never let agents pick their own stance mid-round","Require each rebuttal to explicitly address the prior argument (no topic drift)","Score each round immediately before proceeding to prevent recency bias","Moderator must cite specific debate evidence in the final recommendation","Limit rounds to 3-5 to keep arguments focused; more rounds rarely change outcome"]
error_handling	graceful
streaming	supported
related_skills	["llm-council","advanced-elicitation","plan-generator","sequential-thinking"]
verified	false
lastVerifiedAt	"2026-03-23T00:00:00.000Z"
source	builtin
trust_score	100
provenance_sha	7224309708e7a1c4

Adversarial Debate

Overview

Runs a structured N-round debate between two opposing agents (pro and con) on any decision-requiring topic, then synthesizes a moderator recommendation. Built for architectural decisions, technology choices, security trade-offs, and design reviews — anywhere strong opinions exist and the right answer is non-obvious.

When to Use

• Architecture decision records (ADRs) needing structured justification
• Technology selection (e.g., Redis vs Postgres, REST vs GraphQL)
• Security trade-off analysis (e.g., strict vs permissive auth policy)
• Design review of competing approaches
• Any HIGH/EPIC task where the planner identifies genuine disagreement among specialists

Do not use for:

• Settled best practices (where there is consensus)
• Simple implementation tasks
• Decisions already made and documented in ADRs

Iron Law

NO RECOMMENDATION WITHOUT CITED DEBATE EVIDENCE

The moderator's synthesis MUST reference specific arguments made during rounds. A recommendation that ignores debate content is invalid.

Debate Protocol

Pre-Debate: Stance Assignment

Before any rounds begin, the invoking agent MUST define:

1. Topic — one clear decision question (not open-ended)
2. Pro stance — what the PRO agent advocates for
3. Con stance — what the CON agent argues against / alternative
4. Number of rounds — default 3, max 5
5. Success criteria — what a good recommendation looks like

Example:

Topic: Should we use event sourcing for the order service?
Pro stance: Yes — event sourcing provides audit trail and temporal queries
Con stance: No — event sourcing adds complexity; CRUD with snapshots is sufficient
Rounds: 3

Round Structure

Each round follows this fixed sequence:

Step 1: PRO Argument

PRO agent argues its stance with:
- 1 primary argument (specific, concrete)
- 1 supporting piece of evidence (metric, case study, or first-principles reasoning)
- 1 anticipated objection pre-addressed

Step 2: CON Rebuttal

CON agent responds with:
- Direct refutation of PRO's primary argument (must engage it — no deflection)
- 1 counter-argument from CON's stance
- 1 counter-evidence or challenge to PRO's evidence

Step 3: Round Score

Score each round immediately using this rubric (0–10 per dimension):

Dimension	Description
Specificity	Arguments use concrete examples, not vague claims
Evidence	Claims backed by data, precedent, or first principles
Rebuttal Quality	Directly addresses opposing argument, no deflection
Relevance	Arguments stay on the stated topic

Round Score = mean(Specificity, Evidence, Rebuttal Quality, Relevance)

Track cumulative scores per agent across rounds.

Final Synthesis (Moderator)

After all rounds, the moderator agent produces a structured synthesis:

## Debate Summary

**Topic:** [restate topic]
**Rounds completed:** N
**PRO total score:** X.X/10
**CON total score:** X.X/10

## Key Arguments

**Strongest PRO argument:** [cite round + argument]
**Strongest CON argument:** [cite round + argument]

## Decision Factors

List 3-5 factors that should determine which stance wins in context:

1. Factor: [e.g., team expertise] → Favors: [PRO/CON/neutral]
2. ...

## Recommendation

**Decision:** [clear recommendation — not "it depends"]
**Confidence:** [High / Medium / Low]
**Rationale:** [2-3 sentences citing specific debate evidence from rounds]

## Conditions / Caveats

[Any conditions under which the recommendation should be revisited]

Workflow

Step 0: Load Context and Memory

# Check for prior debate results on same topic
grep -r "adversarial-debate" C:/dev/projects/agent-studio/.claude/context/memory/decisions.md 2>/dev/null | tail -5

Check .claude/context/memory/decisions.md for prior ADRs on same topic. If a prior debate exists, acknowledge it and note whether conditions have changed.

Step 1: Parse and Validate Input

Validate input against schemas/input.schema.json:

node .claude/skills/adversarial-debate/hooks/pre-execute.cjs '{"topic":"...","proStance":"...","conStance":"...","rounds":3}'

Expected output: { "valid": true } or error listing missing fields.

Step 2: Run Debate Rounds

For each round 1..N:

1. PRO agent produces its argument (following Round Structure above)
2. CON agent produces rebuttal (must reference PRO's specific argument)
3. Score the round using the rubric — output a score table
4. Record round to scripts/main.cjs output buffer

Step 3: Moderator Synthesis

After all rounds, produce the Final Synthesis following the template in templates/implementation-template.md.

The moderator MUST:

• Reference at least 2 specific round arguments by round number
• State a single clear recommendation (not "it depends without conditions")
• Assign a confidence level (High/Medium/Low) with justification

Step 4: Persist Decision

Append result to .claude/context/memory/decisions.md:

echo "## [$(date +%Y-%m-%d)] Adversarial Debate: {TOPIC}
- Recommendation: {DECISION}
- Confidence: {CONFIDENCE}
- Rounds: {N}
- Cite: {KEY_EVIDENCE_SUMMARY}" >> .claude/context/memory/decisions.md

Step 5: Emit Observability Event

const { sendEvent } = require('.claude/tools/observability/send-event.cjs');
sendEvent({
  tool_name: 'adversarial-debate',
  agent_id: process.env.AGENT_ID || 'unknown',
  session_id: process.env.SESSION_ID || 'unknown',
  outcome: 'success',
});

Enforcement Hooks

Input validated against schemas/input.schema.json before execution. Output contract defined in schemas/output.schema.json.

Pre-execution hook: hooks/pre-execute.cjs — validates topic, stances, rounds range (1-5). Post-execution hook: hooks/post-execute.cjs — emits observability event via send-event.cjs.

Anti-Patterns

• Never let agents pick their own stances — stance drift produces muddled debates
• Never skip round scoring — unscored debates allow moderator bias in synthesis
• Never produce "it depends" recommendations without explicit conditions listed
• Never use more than 5 rounds — beyond round 3, new arguments rarely emerge
• Never invoke debate for trivial decisions — overhead not justified for settled questions

Related Skills

• llm-council — parallel multi-LLM synthesis (complementary: debate is sequential, council is parallel)
• advanced-elicitation — meta-cognitive reasoning for single-agent analysis
• plan-generator — use debate output as an ADR input to plan-generator
• sequential-thinking — structured problem decomposition for the topic before debate

Memory Protocol

Before starting: Read .claude/context/memory/decisions.md for prior ADRs on related topics.

After completing: Append structured summary to .claude/context/memory/decisions.md with date, topic, recommendation, confidence, and key evidence citations.

Do not write directly to patterns.json, gotchas.json, or open-findings.json — use MemoryRecord tool.