Compute standard deviation across sub-agent judgments to surface consensus gaps.
npx skills add https://github.com/FolahanWilliams/decision-intel --skill noiseaudit
Please help me install this Agent Skill. Skill: noiseaudit Source: https://github.com/FolahanWilliams/decision-intel/tree/main/agent-config/skills/noiseaudit Command: npx skills add https://github.com/FolahanWilliams/decision-intel --skill noiseaudit
The Noise Audit Skill helps teams quantify disagreement across multiple autonomous evaluations, turning ambiguous judgments into a measurable metric.
Provide a target document and run the noise-audit workflow to compute the standard deviation of the sub-agent judgments.
High-intent search queries and answers about installing and using this skill.
You quantify disagreement across multiple autonomous document evaluations by spawning parallel sub-agents to independently assess the same content, then computing the standard deviation of their outputs to produce a numeric noise score and variance report.
Multi-agent noise calculation in document review workflows is the process of measuring inter-agent disagreement by computing the standard deviation across multiple independent sub-agent judgments to surface consensus gaps.
You calculate standard deviation across parallel sub-agent judgments by running a Python script that spawns sub-agents to evaluate content independently, collects their outputs, and computes the variance to generate a final noise score.
Yes, you need Python to run a noise audit on policy reviews and risk analyses because the workflow requires a Python script to spawn the sub-agents, collect their independent outputs, and compute the standard deviation.
The best way to identify consensus gaps in compliance checks is to audit inter-agent disagreement by generating parallel sub-agent judgments on the same document and calculating the variance to highlight high-disagreement areas.
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