control-effectiveness

Assess governance and compliance controls against regulatory obligations using evidence-based evaluation.

Updated Jun 20, 2026
One-click install
npx skills add https://github.com/Sigmacodeat/subsumio-web --skill control-effectiveness
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: control-effectiveness
Source: https://github.com/Sigmacodeat/subsumio-web/tree/main/server/skills/control-effectiveness
Command: npx skills add https://github.com/Sigmacodeat/subsumio-web --skill control-effectiveness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps compliance teams determine whether governance and risk controls are properly designed and actually operating effectively, reducing uncertainty around regulatory readiness.

Core Features & Use Cases

  • Control Assessment: Evaluates both design effectiveness and operating effectiveness against obligations such as GDPR, AML/GwG, EU AI Act, and ISO 27001.
  • Evidence-Based Ratings: Produces effective, partially effective, or ineffective assessments with documented evidence basis and remediation actions.
  • Use Case: A compliance officer can review an AI governance control, verify available evidence such as logs and records, and create a sign-off-ready effectiveness assessment.

Quick Start

Use the control-effectiveness skill to assess whether this control satisfies its regulatory obligation and identify any evidence gaps and remediation steps.

Frequently Asked Questions about control-effectiveness

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

FAQPage Schema
How do I assess control effectiveness for GDPR and ISO 27001 compliance?

Assess control effectiveness for GDPR and ISO 27001 by evaluating design and operating effectiveness against required obligations using evidence-based analysis. This process produces effective, partially effective, or ineffective ratings with documented evidence to ensure regulatory readiness.

What is evidence-based control testing in GRC workflows?

Evidence-based control testing in GRC workflows is the process of verifying governance and risk controls through documented evidence like logs and records. It determines whether controls are properly designed and actually operating to reduce regulatory uncertainty.

Can I use this control assessment for EU AI Act and AML regulatory reviews?

Yes, you can use this control assessment for EU AI Act and AML/GwG regulatory reviews. It applies to GRC workflows by evaluating controls against specific regulatory obligations and generating sign-off ready compliance assessments.

How do I identify evidence gaps during internal control reviews?

Identify evidence gaps during internal control reviews by mapping structured controls to their required obligations and analyzing available design and operating evidence. This process highlights missing documentation and generates remediation tracking actions.

What is the best way to track remediation for ineffective compliance controls?

The best way to track remediation for ineffective compliance controls is through structured effectiveness ratings that document the evidence basis. This generates specific remediation actions and integrates them into a compliance sign-off workflow.