an-anti-hallucination-framework

What problem does it solve?

AI task outputs can be unreliable or misleading; this framework enforces physical evidence and gatekeeping to prevent hallucinated success reports.

Core Features & Use Cases

• Pre-Execution Baselines: Establish environment state and inputs before any action.
• Tool-Driven Execution: Run commands with explicit pre-checks and post-verifications to prove outcomes.
• Structured Verification: Produce a no-story report detailing exact commands, raw outputs, and conclusions to ensure auditability.
• Use Case: In code generation or automation tasks, verify each step with concrete evidence before progressing.

Quick Start

Walk through a sample task by performing a pre-execution baseline, executing a tool-driven action, and presenting the verifiable results with explicit user approval.