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Productivité · SKILL.md

consensus-arbitration

consensus-arbitration

When two or more agents disagree on a decision they each own a piece of, drive the conflict to a single decision via a fixed arbitration rule — never let the conflict silently linger.

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name: consensus-arbitration description: "When two or more agents disagree on a decision they each own a piece of, drive the conflict to a single decision via a fixed arbitration rule — never let the conflict silently linger."

Consensus & arbitration

Multi-agent systems often have silent disagreements that surface as inconsistent outputs to the user. This skill resolves them on purpose.

When to invoke

  • Two agents propose conflicting plans for the same task.
  • A reviewer agent (e.g., shadow-code-reviewer) and the author agent disagree on whether to ship.
  • Two roles each claim ownership of the same artefact (a doc, a ticket, a code path).

The arbitration rule

In priority order:

  1. Hard policy beats opinion. If a policy or compliance constraint applies, that wins. Cite the policy explicitly.
  2. The role with the closest scope wins. A frontend-developer wins on UI choice; a backend-developer wins on schema choice; a security-specialist wins on threat model. If unclear, the manager (crew-manager-pattern) decides.
  3. Existing precedent beats novelty. If the codebase / playbook already has a pattern for this, keep it. Novelty needs explicit user buy-in.
  4. If still unresolved, escalate to a human. Frame the decision as a short, neutral writeup with the two options, the trade-off, and your default if no answer arrives by a deadline (see clarification-checkpoints).

The arbitration write-up

Whichever agent is hosting the arbitration writes one message:

Decision: <one sentence>
Rule applied: <which of the rules above, or "human escalation">
Loser's concern preserved: <one sentence — what we lose by not picking the other option, so we can revisit later>
Owner: <agent or human that will execute the decision>

Anti-patterns

  • Letting the conflict linger and producing inconsistent outputs.
  • Two agents quietly editing the same file with conflicting intents.
  • Skipping the loser's-concern note — that's what catches regressions in retrospectives.
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