rdr-research

Record classified research findings with sources in RDR workflows.

4|Updated Feb 21, 2026

Search Tags:#planning #documentation #research #memory #evidence #classification #rdr

Ready to run

npx skills add https://github.com/Hellblazer/nexus --skill rdr-research

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rdr-research
Source: https://github.com/Hellblazer/nexus/tree/main/nx/skills/rdr-research
Command: npx skills add https://github.com/Hellblazer/nexus --skill rdr-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams capture, classify, and trace structured research findings for active Research-Design-Review (RDR) workflows.

Core Features & Use Cases

Record research findings with systematic classification (Verified, Documented, Assumed) and attach supporting sources.
Automatically generate sequential RDR entries and update central narratives and memory records.
Coordinate agent-assisted evidence gathering and governance during RDR planning to maintain traceability.

Quick Start

Record a new RDR finding with a classification and source using the rdr-research workflow.

Frequently Asked Questions about rdr-research

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

FAQPage Schema

How do I capture and classify research findings in an RDR workflow?▼

To capture research findings in an RDR workflow, you record discoveries using a frontmatter-driven metadata model that classifies entries as Verified, Documented, or Assumed, while attaching traceable evidence sources to each sequential ID.

What is the best way to maintain traceability for evidence during research planning?▼

Maintaining traceability for research planning involves linking evidence sources directly to classified findings in a central repository, applying a structured metadata model to ensure every discovery record remains verifiable.

Can I automatically generate sequential IDs for structured research entries?▼

Yes, you can automatically generate sequential IDs for structured research entries. The workflow uses a frontmatter-driven metadata model to assign traceable identifiers and update central memory records sequentially.

Does this research tracking approach support agent-assisted evidence gathering?▼

Yes, this research tracking approach supports agent-assisted evidence gathering. It coordinates agents during RDR planning to systematically record findings, verify classification status, and update the central repository.

When do I need structured research tracking for RDR-driven work?▼

You need structured research tracking for RDR-driven work when you must systematically capture discoveries, classify their verification status, and link supporting evidence sources to maintain governance across active planning contexts.