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aif-grounded

Enforce evidence-based answers with a 100/100 confidence gate and missing-evidence checklist.

Updated Mar 4, 2026
Search Tags:#verification#uncertainty#data-integrity#evidence-based#trust-safety#ai-governance#confidence-gate
One-click install
npx skills add https://github.com/mind-systems/mind_context --skill aif-grounded-mind-systems
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aif-grounded
Source: https://github.com/mind-systems/mind_context/tree/main/.claude/skills/aif-grounded
Command: npx skills add https://github.com/mind-systems/mind_context --skill aif-grounded-mind-systems

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Grounded reliability gate that minimizes guesswork by enforcing evidence-based reasoning, explicit uncertainty, and a policy of 'insufficient information' when confidence is not 100%.

Core Features & Use Cases

  • Enforces a 100/100 confidence requirement before final answers.
  • Outputs a concise 'what’s missing' checklist to guide users on what is needed to reach full confidence.
  • Applicable in high-stakes domains (security, finance, legal, medical-adjacent), research tasks, and any scenario requiring traceable reasoning.

Quick Start

Respond with a fully sourced answer only when confidence is 100/100; if not, return a short checklist of missing evidence.

Frequently Asked Questions about aif-grounded

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

FAQPage Schema
How do I enforce evidence-based reasoning and prevent AI from guessing in high-stakes domains?▼

Evidence-based reasoning requires a formal verification gate that enforces explicit uncertainty, demanding mandatory evidence listing and a 'what's missing' checklist before finalizing any answer to prevent guesswork.

What happens when AI confidence is not 100/100 for critical data integrity tasks?▼

When confidence is not 100/100, the system outputs a concise 'what's missing' checklist instead of a final answer, guiding users on exactly what required evidence is needed to reach full confidence.

How do I surface known unknowns and required evidence during AI governance verification?▼

AI governance verification surfaces known unknowns by implementing a formal workflow that mandates listing all required evidence and generating a 'what's missing' checklist before any answer is finalized.

When do I need a confidence gate for traceable reasoning in research tasks?▼

A confidence gate is needed for research tasks and scenarios with changing facts, ensuring traceable reasoning by blocking outputs until a 100/100 confidence threshold and sufficient evidence are met.

Does this evidence-based verification approach work for trust and safety scenarios with changing facts?▼

Yes, this approach applies directly to trust and safety scenarios with changing facts by enforcing a 100/100 confidence requirement and returning a missing evidence checklist when data is insufficient.

What is the best way to implement a formal verification workflow for AI-generated answers?▼

The best way to implement formal verification is applying a strict 100/100 confidence gate that mandates evidence listing and a 'what's missing' checklist, returning 'insufficient information' when confidence is lower.

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