aif-grounded

Require 100/100 confidence and evidence before answering high-stakes questions.

Updated Mar 23, 2024
One-click install
npx skills add https://github.com/Ard2p/sk-bar-site --skill aif-grounded-ard2p
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aif-grounded
Source: https://github.com/Ard2p/sk-bar-site/tree/main/.cursor/skills/aif-grounded
Command: npx skills add https://github.com/Ard2p/sk-bar-site --skill aif-grounded-ard2p

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reliability gate that prevents guessing by requiring explicit evidence and 100/100 confidence before presenting conclusions.

Core Features & Use Cases

  • Enforces evidence-based reasoning and explicit uncertainty for high-stakes questions.
  • Outputs a concise “what’s missing” checklist when confidence is below 100.
  • Supports project-specific skill-context rules to tailor behavior and guarantees.

Quick Start

Provide an answer only after achieving 100/100 confidence based on available evidence.

Frequently Asked Questions about aif-grounded

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

FAQPage Schema
How do I prevent AI from guessing on high-stakes security and legal questions?▼

The reliability gate enforces explicit uncertainty by outputting a concise what's missing checklist when confidence is below 100, detailing the specific evidence needed to reach a grounded conclusion.

What is an evidence-based reliability gate for high-stakes domains?▼

It applies to contexts where facts may change or require verification, such as medical, security, legal, or policy questions, ensuring outputs are grounded rather than guessed.

Can I use project-specific rules to tailor no-guessing behavior for medical or policy questions?▼

This ensures the enforced reasoning and explicit uncertainty requirements align with your specific project constraints and verification needs.

What's the best way to enforce 100/100 confidence before an AI answers?▼

This approach ensures explicit uncertainty is maintained and prevents the AI from presenting conclusions without complete evidence-based verification.

How do I get a checklist of missing evidence when AI confidence is low?▼

This checklist details the exact missing information required to achieve 100/100 confidence and produce a grounded, evidence-based conclusion.

When should I not use an evidence-based reliability gate for AI responses?▼

Applying this no-guessing constraint to low-stakes contexts may unnecessarily block answers by demanding explicit evidence where uncertainty is acceptable.