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Skill

/collect-independent-judgments

Collect judgments/rankings from multiple genuinely independent evaluators or perspectives before exposing any peer outputs, preserving ballots and rationale for later social-choice aggregation.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill collect-independent-judgments --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/collect-independent-judgments

Context preview

The summary Claude sees to decide when to auto-load this skill.

Collect judgments/rankings from multiple genuinely independent evaluators or perspectives before exposing any peer outputs, preserving ballots and rationale for later social-choice aggregation.

SKILL.md

collect-independent-judgments.SKILL.md
name: collect-independent-judgments
description: "Collect judgments/rankings from multiple genuinely independent evaluators or perspectives before exposing any peer outputs, preserving ballots and rationale for later social-choice aggregation."

collect-independent-judgments

Purpose

Collect blinded judgments from genuinely independent evaluators or perspectives before exposing peer outputs.

Input contract

required: [judgment_prompt, evaluator_roster, candidate_set, independence_rules]
optional: [rubric, response_deadline, abstention_policy]
constraints: [evaluators receive no peer judgments; each ballot preserves rationale and provenance]

Procedure

1. Freeze the candidate set, rubric, prompt, and independence rules before collection. 2. Obtain one sealed judgment and rationale per evaluator, recording abstentions and unavailable evidence. 3. Check for shared data, model, prompt, or deliberation leakage before unblinding. 4. Unblind only after collection, then return ballots, independence flags, and an aggregation-ready dataset.

If independent judgments are complete and their provenance is preserved, consider `aggregate-ranking` as the next tactic.

Output contract

produces: [sealed_ballots, independence_audit, rationale_set, aggregation_dataset]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]

Quality gates

  • A-class gate: declared universe = all commissioned evaluator ballots; numerator = ballots complete, provenance-linked, and leakage-free; batch increment = one sealed ballot; stopping reason = roster exhausted or a declared quorum reached with all exclusions documented; source references = evaluator, prompt, data, and ballot IDs; direction/threshold reason = independence confidence decreases with shared inputs or peer exposure.
  • No aggregation before the leakage check is complete.

Failure and counterexamples

Do not count multiple roles using the same evidence as independent judgments. An abstention is a recorded outcome, not a missing pass.

Provenance map

  • resolved: collective-adjudication
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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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