scoring-reviewer

Validate scoring changes against psychometric standards with structured PASS/FAIL reports.

1|Updated Mar 8, 2026
One-click install
npx skills add https://github.com/aibilitycz/superpowered-toolkit --skill scoring-reviewer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: scoring-reviewer
Source: https://github.com/aibilitycz/superpowered-toolkit/tree/main/plugins/super-knowledge/skills/scoring-reviewer
Command: npx skills add https://github.com/aibilitycz/superpowered-toolkit --skill scoring-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps ensure that changes to scoring models or configurations do not degrade measurement quality, fairness, or interpretability by providing a structured validation workflow.

Core Features & Use Cases

  • Validation checks for reliability, validity, and fairness of scoring changes.
  • Audit-ready workflow that surfaces potential issues and rationale for each finding.
  • Guidance and prerequisites referencing domain files to align with psychometric standards. Use Case: A data team revises a scoring formula for a cognitive assessment and runs this skill to verify the change maintains composite reliability and DIF fairness before deployment.

Quick Start

Provide a scoring change description or diff to trigger the validation and receive a structured PASS/FAIL report.

Frequently Asked Questions about scoring-reviewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate scoring changes for psychometric assessments?

You validate scoring changes by providing a scoring change description or diff to trigger a structured validation workflow that checks psychometric standards and returns a PASS/FAIL report. This ensures measurement quality, fairness, and interpretability are maintained before deployment.

What is psychometric validation for scoring formula adjustments?

Psychometric validation for scoring formula adjustments is a structured process that verifies changes to scoring models do not degrade measurement quality. It applies explicit checks for composite reliability, weight justification, normative reference integrity, DIF impact, and backward compatibility.

When do I need to run a DIF impact and reliability check on composite scores?

You need to run a DIF impact and reliability check on composite scores whenever you revise scoring formulas, adjust weights, modify thresholds, or alter composite scores within psychometric assessments to ensure fairness and measurement quality are preserved.

Does this scoring validation workflow generate an audit-ready report for weight adjustments?

Yes, the scoring validation workflow generates an audit-ready report that surfaces potential issues and provides rationale for each finding when validating weight adjustments, ensuring accountability and alignment with psychometric standards.

What limitations should I consider when validating backward compatibility for scoring thresholds?

When validating backward compatibility for scoring thresholds, consider that the validation requires explicit checks for composite reliability, weight justification, normative reference integrity, and DIF impact, which may surface issues that block deployment if standards are not met.