experiment-audit

Name: experiment-audit
Availability: InStock
Author: dogekiki

Audit experimental integrity by cross-referencing scripts, results, and claims against fraud patterns.

1|Updated Jul 21, 2026

Search Tags:#research #verification #audit #integrity #experiment #fraud detection

Ready to run

npx skills add https://github.com/dogekiki/SP-test --skill experiment-audit-dogekiki

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-audit
Source: https://github.com/dogekiki/SP-test/tree/main/.trae/skills/experiment-audit
Command: npx skills add https://github.com/dogekiki/SP-test --skill experiment-audit-dogekiki

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill prevents fraudulent or misleading experimental results by independently auditing evaluation scripts, result files, and paper claims for common failure modes like fake ground truth and score normalization fraud.

Core Features & Use Cases

Cross-Model Integrity Verification: Uses an external reviewer backend to perform adversarial checks on experimental data.
Fraud Detection: Identifies synthetic ground truth, phantom results, and insufficient scope in research evaluations.
Use Case: After running a series of model benchmarks, use this skill to audit the results before finalizing your report to ensure all claims are backed by valid, non-compromised evidence.

Quick Start

Run the experiment-audit skill on the current directory to verify the integrity of the latest experimental results.

Frequently Asked Questions about experiment-audit

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

FAQPage Schema

How do I detect fraudulent or manipulated results in machine learning experiments?▼

To detect fraudulent or manipulated results in machine learning experiments, you can audit experimental integrity by cross-referencing evaluation scripts, result files, and paper claims against known fraud patterns like synthetic ground truth and score normalization fraud.

What is experiment integrity verification and how does it work?▼

Experiment integrity verification is the process of independently auditing evaluation scripts and result files to identify common failure modes. It works by applying adversarial analysis to project artifacts to ensure model performance claims are backed by valid evidence.

How do I audit model benchmark results before finalizing a research report?▼

To audit model benchmark results before finalizing a research report, run an audit on the current directory to cross-reference evaluation scripts and result files, ensuring all claims are backed by non-compromised evidence.

Do I need an external reviewer backend to perform adversarial analysis of experimental data?▼

Yes, you need an external reviewer backend like Codex or manual review tools to perform adversarial analysis of experimental data. This integration is required to execute cross-model integrity verification on project artifacts.

What types of research fraud can be identified during an experiment audit?▼

An experiment audit can identify several types of research fraud, including synthetic ground truth, phantom results, score normalization fraud, and insufficient scope in research evaluations.

Can I use automated fraud detection for research and development workflows?▼

Yes, you can use automated fraud detection for research and development workflows where model performance claims require independent verification. It audits experimental integrity by cross-referencing evaluation scripts and result files.