reporting-and-archiving-findings

Verify reproducibility, execute chosen archival actions, and archive code, data, and environment locks.

282|26|Updated May 28, 2026
One-click install
npx skills add https://github.com/K-Dense-AI/science-superpowers --skill reporting-and-archiving-findings
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: reporting-and-archiving-findings
Source: https://github.com/K-Dense-AI/science-superpowers/tree/main/skills/reporting-and-archiving-findings
Command: npx skills add https://github.com/K-Dense-AI/science-superpowers --skill reporting-and-archiving-findings

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides teams to wrap up analyses by verifying reproducibility, presenting options, executing the chosen path, and archiving code, data, environment, and pre-registration for full reproducibility.

Core Features & Use Cases

  • Reproducibility verification: Re-run pipelines from raw data with fixed seeds to confirm results.
  • Option presentation & execution: Provide a clear menu of actions (merge, report, keep, discard) and perform the chosen one while preserving provenance.
  • Comprehensive archiving: Collect and store code, data provenance, environment lockfiles, seed values, and pre-registrations to enable future reproduction.

Quick Start

Archive all materials and publish a report once reproducibility is verified.

Frequently Asked Questions about reporting-and-archiving-findings

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

FAQPage Schema
How do I archive research data and code for full reproducibility?

Reproducibility verification involves re-running pipelines from raw data with fixed seeds to confirm results before archiving code, data provenance, environment lockfiles, seed values, and pre-registrations.

What is pre-registration alignment in research reporting workflows?

Pre-registration alignment ensures that documented analysis paths and selected options transparently match the pre-registered research plan, maintaining data integrity and immutable reproducibility throughout the reporting process.

How do I verify reproducibility in an analysis pipeline before archiving?

Verify reproducibility by re-running pipelines from raw data with fixed seeds to confirm results match, then execute chosen reporting actions like merging or discarding while preserving provenance.

What's the best way to present analysis options to a research team before archiving?

Present a clear menu of actions such as merge, report, keep, or discard, allowing the team to execute the chosen path while preserving provenance and reproducibility artifacts.

Do I need environment lockfiles and seed values for research archiving?

Yes, environment lockfiles and seed values are essential archival artifacts required to lock the computational environment and enable exact future reproduction of validated analyses.

Can I use reporting workflows for pre-registered studies without fixed seeds?

Fixed seeds are required for reproducibility verification; without them, re-running pipelines from raw data cannot confirm consistent results, breaking the immutable reproducibility needed for pre-registration alignment.