yogsoth-ai/de-anthropocentric-research-engine

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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使用方法

复制安装命令，在自己的智能体环境安装前先检查源码包。
从原始文档核对所需工具、凭据和支持的宿主。
说明输入材料与期望交付物，并对照来源检查输出。

npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill collect-independent-judgments

开始前需了解

安装不包含模型订阅、API 额度或访问文件的权限。
仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。

仓库原始内容

按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。

collect-independent-judgments

Purpose

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

Input contract

yaml

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

Freeze the candidate set, rubric, prompt, and independence rules before collection.
Obtain one sealed judgment and rationale per evaluator, recording abstentions and unavailable evidence.
Check for shared data, model, prompt, or deliberation leakage before unblinding.
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

yaml

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.