Risk Tiering

Score ML model governance risk tiers using rubric-defined axes and controls.

Updated Jan 30, 2026

Search Tags:#models #compliance #governance #evidence #risk #controls #tiering

Ready to run

npx skills add https://github.com/gtylee/CodexGAS --skill risk-tiering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Risk Tiering
Source: https://github.com/gtylee/CodexGAS/tree/main/modelgas/skills/risk_tiering
Command: npx skills add https://github.com/gtylee/CodexGAS --skill risk-tiering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables governance teams to assign a consistent risk tier to ML models by scoring them across key axes (financial impact, reliance, usage, complexity, and mitigation strength) and linking the results to required controls.

Core Features & Use Cases

Axis-based risk scoring: compute scores per axis and a total tier using a rubric.
Control mapping: translate tier to mandated governance controls with evidence-backed justification.
Evidence-driven decision support: produce a structured artifact suitable for audits and reviews.
Use Case: A model with moderate financial impact and high reliance is assigned Tier 2 and required controls; integrated with other skills for remediation.

Quick Start

Run risk_tiering on input IR and rubric data, then review the produced findings and control mapping.

Frequently Asked Questions about Risk Tiering

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

FAQPage Schema

How do I assign a risk tier to an ML model for governance compliance?▼

ML model governance risk tiering scores models across financial impact, reliance, usage, complexity, and mitigation axes. The total score maps to a specific tier using a predefined rubric, linking to mandated controls.

What is evidence-driven risk tiering for machine learning models?▼

Evidence-driven risk tiering evaluates ML model axis scores and total scores against a predefined rubric and control mappings. It produces structured schema-compliant findings with rationale for audits and incident reviews.

How do I map governance controls to ML model risk tiers?▼

Control mapping translates the assigned risk tier into mandated controls across product, data, and deployment scenarios. It uses control mappings to provide evidence-backed justification for required safeguards.

Does model risk tiering require specific input data formats?▼

Model risk tiering requires input IR and rubric data to execute the scoring process. It produces schema.json compliant findings containing axis_scores, total_score, tier, and rationale fields.

Can I use risk tiering for model audits and incident reviews?▼

Yes, risk tiering applies to model governance workflows, audits, and incident reviews where evidence-backed tiering determines required safeguards across product, data, and deployment scenarios.

What's the best way to score model reliance and complexity for risk assessment?▼

The best way is using an axis-based risk scoring approach that computes scores per axis and a total tier using a predefined rubric. This ensures consistent evaluation of model reliance and complexity.