calibration

Separate facts from assumptions and update Knowns/Unknowns matrices.

4|Updated Jan 28, 2026
One-click install
npx skills add https://github.com/wme3/DeliberateDecisions --skill calibration
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: calibration
Source: https://github.com/wme3/DeliberateDecisions/tree/main/skills/calibration
Command: npx skills add https://github.com/wme3/DeliberateDecisions --skill calibration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Gate 4 provides a disciplined process to consolidate what we know versus what we assume, ensuring decisions are anchored in evidence and clearly tracked uncertainties.

Core Features & Use Cases

  • Distinguish verified facts from unverified assumptions to reduce decision risk
  • Update Knowns/Unknowns matrices with new findings and unanswered questions
  • Compile a comprehensive Assumption Inventory with criticality, testability, and fallback plans
  • Generate a Calibration Log for traceability, risk assessment, and audit readiness

Quick Start

Begin Gate 4 by reviewing claims, separating facts from assumptions, and updating the Knowns/Unknowns matrix.

Frequently Asked Questions about calibration

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

FAQPage Schema
How do I separate verified facts from assumptions to reduce decision risk?

To separate facts from assumptions, consolidate gathered evidence by reviewing claims and categorizing them into verified facts or unverified assumptions. This process anchors decisions in evidence and produces an assumption inventory to reduce decision risk.

What is an assumption inventory and how does it support risk management?

An assumption inventory is a comprehensive log of unverified assumptions compiled with criticality, testability, and fallback plans. It supports risk management by tracking uncertainties and logging critical risks for follow-up, ensuring audit readiness and traceability.

How do I update Knowns and Unknowns matrices with new research findings?

Update Knowns and Unknowns matrices by reviewing new evidence gathered across research gates and categorizing findings into verified facts or unanswered questions. This calibration enforces requirements for tracking uncertainties and strengthens decision workflows.

When do I need to calibrate facts versus assumptions in a decision workflow?

Calibration is needed after gathering evidence across research gates and before finalizing decisions. It enforces requirements like updating Knowns and Unknowns, producing an assumption inventory, and logging critical risks to ensure decisions are anchored in evidence.

What's the best way to generate a calibration log for traceability and audit readiness?

Generate a calibration log by consolidating information through separating facts from assumptions, updating the Knowns and Unknowns matrix, and compiling an assumption inventory. This log provides traceability, risk assessment, and audit readiness for decision workflows.

Does this calibration process work for decision workflows that require rigorous evidence gathering?

Yes, the calibration process applies to decision workflows requiring rigorous calibration after evidence gathering. It consolidates information by separating facts from assumptions, updating Knowns and Unknowns, and producing an assumption inventory for risk assessment.