consensus-measurement
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Compute consensus score from collected judgments using the appropriate statistical method.
In this repository119 other files it gives its agents.
- adaptive-pair-selectionSkill
- adversarial-debate-protocolSkill
- advocate-constructionSkill
- aggregation-methodSkill
- alternative-scoringSkill
- appropriateness-boundingSkill
- argument-crystallizationSkill
- argument-extractionSkill
- assumption-challengeSkill
- assumption-excavationSkill
- assumption-extractionSkill
- ballot-collectionSkill
- best-option-selectionSkill
- bottleneck-identificationSkill
- category-sortingSkill
- cluster-analysisSkill
- coherence-diagnosisSkill
- collective-adjudicationSkill
- comparative-feasibility-rankingSkill
- comparison-executorSkill
- conclusion-sensitivitySkill
- conjunctive-filterSkill
- consensus-classificationSkill
- consensus-synthesisSkill
- consistency-audit-loopSkill
- constraint-classificationSkill
- constraint-drillingSkill
- constraint-identificationSkill
- constraint-identification-sopSkill
- convergence-checkSkill
- convergence-distillationSkill
- counter-thesis-constructionSkill
- coverage-scoringSkill
- criteria-interrogationSkill
- criterion-definitionSkill
- critic-attackSkill
- cycle-detectionSkill
- deliberative-calibrationSkill
- dimension-assessmentSkill
- disagreement-cartographySkill
- disagreement-mappingSkill
- disagreement-visualizationSkill
- diversity-maximizationSkill
- dominance-checkSkill
- dynamic-trackingSkill
- efficient-explorationSkill
- feasibility-assessmentSkill
- feasibility-synthesisSkill
- feedback-distributionSkill
- full-rankingSkill
- futures-calibrationSkill
- gate-criteria-definitionSkill
- gate-judgmentSkill
- inconsistency-localizationSkill
- iterative-convergence-roundSkill
- judge-verdictSkill
- judgment-collectionSkill
- maturation-pathway-designSkill
- maturity-diagnosisSkill
- method-sensitivity-reportSkill
- The first 60 shown.
The file
Files
Consensus Measurement
Compute a quantitative consensus score from the collected judgments. Automatically selects the appropriate measurement method based on data type (IQR for continuous, percentage agreement for categorical, Kendall's W for rankings).
Execution
Spawn a subagent that analyzes the judgments array, determines the appropriate consensus metric, computes the score, and reports whether the consensus threshold is met.
Why Subagent
- Statistical computation is a pure function with clear input/output
- Method selection logic is self-contained
- Result feeds directly into round-decision
HARD-GATE
Output MUST contain: consensus_score (numeric), method_used (string), threshold_met (boolean), and interpretation (string). Score must be computed, not estimated.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.