an-anti-hallucination-framework

Name: an-anti-hallucination-framework
Availability: InStock
Author: Shichiyou

Enforce pre-execution baselines and post-verification checks for AI task outputs.

Updated Aug 27, 2026

Search Tags:#verification #quality-assurance #audit-trail #ai-safety #anti-hallucination #tool-driven

Ready to run

npx skills add https://github.com/shichiyou/hermes-agent-001 --skill an-anti-hallucination-framework

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: an-anti-hallucination-framework
Source: https://github.com/shichiyou/hermes-agent-001/tree/main/.devcontainer/hermes-backup/skills/.archive/an-anti-hallucination-framework
Command: npx skills add https://github.com/shichiyou/hermes-agent-001 --skill an-anti-hallucination-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about an-anti-hallucination-framework

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

FAQPage Schema

How do I prevent hallucinated success reports in AI task execution?▼

To prevent hallucinated success in AI task execution, establish pre-execution baselines, run tool-driven actions with explicit checks, and produce verifiable post-action reports. This enforces physical evidence to prove actual outcomes before progressing.

What is a pre-execution baseline in AI verification workflows?▼

A pre-execution baseline in AI verification workflows is the recorded environment state and inputs established before any action runs. It serves as a deterministic reference point to validate post-action results and ensure tool-driven execution accountability.

How do I verify code generation tasks with concrete evidence?▼

You verify code generation tasks with concrete evidence by enforcing a deterministic workflow that runs explicit pre-checks and post-verifications. This produces a no-story report detailing exact commands and raw outputs to ensure auditability.

Can I use this anti-hallucination framework for data processing pipelines?▼

Yes, you can use this anti-hallucination framework for data processing pipelines. It is applicable to any AI-assisted technical task where verification of results is critical, enforcing physical evidence and gatekeeping across development and operations contexts.

Does tool-driven execution work without dependencies for audit trails?▼

Tool-driven execution works without external dependencies to generate audit trails. The framework internally enforces structured verification by capturing raw command outputs and producing a no-story report detailing exact conclusions for auditability.

What's the best way to audit AI automation tasks for quality assurance?▼

The best way to audit AI automation tasks for quality assurance is to enforce a deterministic workflow with pre-execution baselines and verifiable post-action reporting. This grounds task outputs in physical evidence, preventing misleading or unreliable results.