| 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
- State the decision to be made
- Identify the domain (tool selection, architecture, strategy, process, content)
- Select the depth tier
- 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:
- Scores every other proposal from their perspective (1-5)
- Identifies the strongest competitor and why
- Identifies the weakest competitor and the specific flaw
- 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.
