ballot-collection
Ballot Collection gathers independent ranking evaluations of candidates from multiple specified perspectives or judges, with each evaluation generated separately to prevent bias from other judges' outputs. Use this skill when you need authentic comparative rankings across different viewpoints, such as evaluating candidates through diverse criteria, stakeholder perspectives, or evaluation frameworks where independence between raters is essential.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ballot-collection && cp -r /tmp/ballot-collection/skills/ballot-collection ~/.claude/skills/ballot-collectionSKILL.md
# Ballot Collection Collects independent ranking ballots from multiple judges or evaluation perspectives. Each judge produces a complete or partial ranking of the candidates without seeing other judges' rankings. ## Execution Runs as a subagent. Receives candidates and perspective definitions, returns structured ballots. ## Why Subagent Each ballot must be generated independently to prevent anchoring. The subagent evaluates from a single perspective without access to other judges' outputs, ensuring genuine independence. ## HARD-GATE Output MUST contain one ballot per perspective. Each ballot MUST rank all candidates (complete ranking) or explicitly mark unranked candidates. No two ballots may be identical unless perspectives are genuinely indistinguishable.
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems