verifying-results-before-claiming

Name: verifying-results-before-claiming
Availability: InStock
Author: K-Dense

Verify research claims against reproduced outputs from raw data.

282|26|Updated May 28, 2026

Search Tags:#evidence #data-integrity #verify #reproducibility #results #research-methods #p-hacking

Ready to run

npx skills add https://github.com/K-Dense-AI/science-superpowers --skill verifying-results-before-claiming

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: verifying-results-before-claiming
Source: https://github.com/K-Dense-AI/science-superpowers/tree/main/skills/verifying-results-before-claiming
Command: npx skills add https://github.com/K-Dense-AI/science-superpowers --skill verifying-results-before-claiming

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Claiming a finding without fresh verification is dishonesty, not efficiency. Evidence before claims, always.

Core Features & Use Cases

Enforces a rigorous verification gate before reporting results.
Guides how to re-run analyses from raw data with fixed seeds to reproduce outputs.
Applies across research workflows to protect against p-hacking and HARKing.

Quick Start

Run the analysis from the raw data with a fixed seed, read the actual output, and verify that the claim is supported before reporting.

Frequently Asked Questions about verifying-results-before-claiming

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

FAQPage Schema

How do I verify research results before reporting claims?▼

To verify research results before reporting claims, re-run the analysis from immutable raw data with a fixed seed, read the actual outputs, and confirm the reported results are fully supported by the fresh evidence.

What is p-hacking and how does verifying results prevent it?▼

P-hacking is manipulating data analysis to falsely achieve statistical significance. Verifying results prevents it by enforcing a rigorous verification gate that requires reproducing outcomes from raw data before any claim is reported.

How do I reproduce analysis outputs using a fixed seed?▼

Reproducing analysis outputs using a fixed seed requires running the analysis from immutable raw data while setting the random number generator seed to a constant value, then reading the actual output readouts to confirm the findings.

When do I need to verify results against raw data?▼

You need to verify results against raw data in any research workflow where outcomes are reported. It is required before making claims to protect data integrity and ensure reported findings are supported by fresh reproduced evidence.

What is the best way to prevent HARKing in research workflows?▼

The best way to prevent HARKing in research workflows is to apply a strict verification gate that requires re-running analyses from raw data with a fixed seed and reading the actual outputs before reporting any findings.

Can I report findings without reproducing outputs from raw data?▼

No, reporting findings without fresh verification is dishonesty. You must run the analysis from immutable raw data with a fixed seed, read the actual output, and verify the claim is supported by evidence before reporting.