self-test

Evaluate knowledge memory system health across eight dimensions with structured scoring.

11|Updated Feb 6, 2026
One-click install
npx skills add https://github.com/anticorrelator/lore --skill self-test-anticorrelator
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: self-test
Source: https://github.com/anticorrelator/lore/tree/main/skills/self-test
Command: npx skills add https://github.com/anticorrelator/lore --skill self-test-anticorrelator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill rigorously evaluates the effectiveness and reliability of your knowledge memory system, identifying weaknesses and guiding improvements to ensure it actively supports your workflow rather than hindering it.

Core Features & Use Cases

  • Comprehensive Evaluation: Tests 8 critical dimensions of a knowledge system, from orientation and retrieval to thread awareness and plan continuity.
  • Actionable Insights: Produces scored results, identifies regressions, and provides concrete recommendations for improvement.
  • Use Case: Before a major refactor, run this self-test to ensure your knowledge store is up-to-date and accurately reflects the current system architecture, preventing costly mistakes based on outdated information.

Quick Start

Run the self-test skill to evaluate the current knowledge system.

Frequently Asked Questions about self-test

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

FAQPage Schema
How do I evaluate the health and effectiveness of my knowledge memory system?

A knowledge system self-test measures effectiveness across eight dimensions: orientation, retrieval, backlink navigation, thread awareness, plan continuity, knowledge utilization, search functionality, and data freshness. It scores each area to identify regressions and guide improvements.

When should I run an evaluation on my knowledge store?

Run a knowledge store evaluation before a major refactor to verify your data is up-to-date and accurately reflects current system architecture, preventing costly mistakes caused by outdated information.

How do I identify regressions in my knowledge management workflow?

To identify regressions in knowledge management, perform a structured evaluation across dimensions like search functionality and knowledge utilization. This process highlights scoring drops and generates actionable recommendations for system improvement.

Can I test data freshness and retrieval accuracy in my memory system?

Yes, you can test data freshness and retrieval accuracy by running an evaluation that specifically scores these dimensions within your memory system. It checks if your knowledge store actively supports your workflow without hindering it.

What is the best way to improve backlink navigation and thread awareness?

The best way to improve backlink navigation and thread awareness is to use an evaluation tool that scores these specific dimensions. It generates concrete, actionable recommendations to fix structural weaknesses and enhance system reliability.

Why does my knowledge system hinder my workflow instead of supporting it?

Your knowledge system may hinder your workflow due to weaknesses in plan continuity or retrieval effectiveness. Running a self-test evaluates these dimensions, providing structured scoring to identify exactly where the system is failing.