conjecture-criticism

name	conjecture-criticism
description	Adversarial analysis via conjecture and criticism. Spawns parallel agents to independently generate competing approaches, then each agent critiques all others. Consensus emerges from cross-criticism. Use when making recommendations with real alternatives, evaluating options, or when the user says "analyze", "evaluate", "criticize", "compare options", "what am I missing", or "poke holes".
version	1.0.0

Conjecture & Criticism

Philosophy

Knowledge is created through conjecture and criticism, not authority or induction (David Deutsch, The Beginning of Infinity). Every recommendation is a conjecture. Every conjecture deserves genuine criticism from independent perspectives. The strongest idea is the one that survives.

When This Activates

Auto-trigger (baked-in via standing order #16)

• The agent is about to recommend one option over alternatives
• The domain has real consequences (architecture, strategy, process, tool selection)
• Multiple viable approaches exist

Manual trigger

• User invokes /analyze, /evaluate, or /criticize
• User asks "what am I missing", "poke holes", "compare options"

Skip (do not trigger)

• Tactical execution with a single clear path
• Questions with factual answers
• Tasks where the user has already decided and wants execution

Depth Tiers

Tier	When	Agents	Rounds	Output
Quick	Low stakes, tactical	0 (inline self-check)	1	Verdict with brief "considered X but Y"
Moderate	Multiple viable alternatives, real trade-offs	3	2 (generate + all-to-all critique)	Verdict-first + key criticisms
Deep	Architecture, strategy, process, or significant disagreement	4+	3+ (generate + all-to-all + targeted debate)	Verdict + scorecard + killed conjectures

Escalation rule: If moderate-tier agents disagree significantly after cross-critique (no convergence on a winner), auto-escalate to deep.

Calibration over time: Track which decision types benefited from adversarial analysis. Feed this signal to the learning system to improve depth sizing.

Protocol

Phase 1: Frame the Problem

1. State the decision to be made
2. Identify the domain (tool selection, architecture, strategy, process, content)
3. Select the depth tier
4. Announce: "Running conjecture-criticism at [tier] depth."

Phase 2: Assign Perspectives

Analyze the decision domain and assign context-adaptive perspectives. Rules:

• Perspectives must be genuinely distinct (not three ways of saying "is it good")
• At least one perspective must be adversarial to the leading conjecture
• At least one perspective must consider what everyone else is likely to miss
• State assigned perspectives upfront so the user can see how the problem is framed

Example perspective sets by domain:

Domain	Perspectives
Tool/library selection	Simplicity, Ecosystem maturity, Migration cost
Architecture	Scalability, Operational complexity, Time-to-ship
Strategy	Risk exposure, Opportunity cost, Second-order effects
Process design	Adoption friction, Failure modes, Maintenance burden
Content/brand	Audience resonance, Differentiation, Sustainability

These are examples, not fixed. Derive perspectives from the actual decision.

Phase 3: Generate (Parallel Agents)

Spawn N parallel sub-agents using the Agent tool. Each agent receives:

• The decision context
• Their assigned perspective
• Instructions to independently generate their best approach

Use the agent-prompt template. All agents run in parallel with no shared context. This ensures genuinely independent thinking.

Critical: Each agent generates from their perspective AND provides a clear thesis for why their approach is best. They have skin in the game.

Phase 4: Cross-Critique (Parallel Agents)

Each agent receives ALL other agents' proposals and critiques them.

• At moderate tier (3 agents): 3 agents each critique 2 proposals = 6 critique passes, all running in parallel. Wall-clock time = 1 round.
• At deep tier (4+ agents): same pattern, more passes.

Use the cross-critique template. Each agent:

1. Scores every other proposal from their perspective (1-5)
2. Identifies the strongest competitor and why
3. Identifies the weakest competitor and the specific flaw
4. States whether they still believe their own proposal is best, or concede

Phase 5: Debate (Deep Tier Only)

Status: Deferred. The debate-round template is not yet implemented. Deep tier auto-escalation is disabled until this is shipped. Use moderate tier maximum.

After all-to-all critique, identify points of disagreement. Spawn targeted debate rounds ONLY between agents that disagreed.

• No wasted rounds on settled points
• Each debate agent receives their own assessment, the opposing assessment, and the specific point of contention
• Use the debate-round template

Convergence rule: If all agents agree on a winner after any round, stop. Deadlock rule: If no convergence after 3 rounds, present the deadlock to the user with each position's strongest argument. Let the user decide.

Phase 6: Synthesize

The main agent collects all outputs and produces:

Verdict-first output (default):

## Verdict
[Recommendation in 1-2 sentences]

## Why This Wins
[2-3 key reasons, drawing from cross-critique results]

## Key Criticisms That Shaped This
[The strongest criticisms and how they influenced the verdict]

## Runner-Up
[Second-best option and why it lost]

Scorecard (on request or for deep tier):

| Option | [Perspective 1] | [Perspective 2] | [Perspective 3] | Aggregate | Verdict |
|--------|---|---|---|---|---|

Killed conjectures (deep tier):

## Killed Conjectures
### [Option]: [Name]
**Eliminated because:** [Specific criticism from cross-critique]
**Lesson:** [Reusable insight]

Phase 7: Feed Learning System

After synthesis, produce learning artifacts:

• Killed conjectures become instinct candidates with trigger and action
• Successful criticism patterns become reusable evaluation criteria
• Depth tier appropriateness feeds calibration

Format for instinct candidate:

id: [generated]
trigger: "when recommending [domain/pattern]"
action: "avoid [killed approach] because [reason]"
confidence: 0.5
domain: [decision domain]
source: "conjecture-criticism"
evidence: "[date] -- [decision context]"

Write instinct candidates to the observations queue for the learning system to process. Do not create instincts directly.