Skip to main content
Spawn Monk A and Monk B as separate subagent sessions. Use claude -p (or your environment’s equivalent for spawning an independent agent) so each gets a clean context with full belief commitment.

​
Spawning command pattern

# Example for Claude Code:
echo "[MONK A PROMPT]" | claude -p --allowedTools web_search,web_fetch,read_file > monk_a_output.md
echo "[MONK B PROMPT]" | claude -p --allowedTools web_search,web_fetch,read_file > monk_b_output.md
These can run in parallel if your environment supports it.

​
Efficiency with context briefing

With the context briefing in place, monks need only 2-3 targeted searches each (vs. 15-25 without it).
For personal/values domains, monks may need zero additional searches — the briefing contains the user’s own material which is the primary evidence base.

​
After both complete: Quality checks

Read both outputs carefully. Check:
1

Full conviction check

Did each monk actually believe fully, or did it hedge?A hedging monk has failed its core function.
2

Framing check

Did the framing corrections work, or did a monk fall into the degenerate framing?
3

Evidence grounding

Are the arguments grounded in specific evidence (from the briefing or their own searches)?

​
Decorrelation check: Critical quality gate

Verify the monks actually diverged. The skill’s value comes from structurally uncorrelated exploration of the problem space.

Evidence sources

Do the monks cite different evidence, or substantially overlapping sources?

Conceptual vocabulary

Do they frame the problem using different conceptual vocabularies?

Unstated assumptions

Do their unstated assumptions diverge, or do they share the same background framework?

Perspective recognition

Would a reader recognize these as genuinely different perspectives, or the same perspective with different conclusions bolted on?
If decorrelation is low — the monks are in “same framework, different conclusions” mode — consider reformulating the belief burdens to force genuinely different conceptual frames, not just different positions within one frame.
See Decorrelation for detailed guidance on strengthening decorrelation and what to do when it’s low.

​
If a monk’s output is off-base

Prefer restarting with a revised prompt over nudging. Fresh context with better instructions produces better results than correcting a monk that’s lost its conviction.

​
Present outputs to the user

Present both outputs with a brief re-explanation:
Here are two essays — each one fully committed to one side of the tension we’ve been discussing. They’re called “Electric Monks” because their job is to believe these positions so you don’t have to. That frees you to read both and notice the structure of the disagreement from the outside. A few important things as you read:
  • These will get things wrong. The monks are working from what I told them, and I may have gotten your situation wrong, or they may have made assumptions that don’t match your reality. That’s expected — especially in this first round.
  • Correct them freely. If a monk says something that’s off-base, tell me. “That’s not how it works” or “they’re missing that…” — these corrections are the most valuable input in the entire process. The synthesis can only be as good as the positions it’s built on.
  • The first round is the least insightful. Think of it as calibration. Each subsequent round gets sharper, more specific, and more tuned to what you actually care about. The real breakthroughs usually come in rounds 2 and 3, once the process has dug past the obvious framing into the deeper tensions.
  • Add anything that occurs to you. New ideas, things neither monk mentioned, gut feelings you can’t fully articulate — all of it is useful. You know your situation better than any of us.

​
Critical user checkpoint

Then ask:
  1. Do these capture the positions accurately?
  2. “Is there a claim either monk makes that should be tested against evidence neither has considered?”
This is the second high-leverage intervention point. In testing, users identified claims that sounded plausible but collapsed under scrutiny when tested against comparison classes the monks didn’t consider. Catching this before synthesis prevents the entire downstream analysis from being built on an untested assumption.
If the user identifies a testable claim, run a targeted research agent to check it. This is cheap (~25-50K tokens) and can fundamentally change the quality of the synthesis.

​
Next: Determinate negation

Phase 4: Determinate negation

Analyze the structural contradiction between the monks

Build docs developers (and LLMs) love

Get started for freeTalk to us