mnemonic-progressive-disclosure

Recall information in tiered levels based on user intent.

20|4|Updated Jan 24, 2026
One-click install
npx skills add https://github.com/zircote/mnemonic --skill mnemonic-progressive-disclosure
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mnemonic-progressive-disclosure
Source: https://github.com/zircote/mnemonic/tree/main/skills/mnemonic-progressive-disclosure
Command: npx skills add https://github.com/zircote/mnemonic --skill mnemonic-progressive-disclosure

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of recalling information from memory at the appropriate level of detail, preventing information overload and ensuring users get exactly the context they need, when they need it.

Core Features & Use Cases

  • Tiered Recall: Provides information in three levels: Quick Answer, Context, and Comprehensive Detail.
  • Contextual Relevance: Automatically determines the best level of detail based on user intent (e.g., simple question vs. implementation task).
  • Use Case: When asked "What database are we using?", it provides a concise answer. If the user then asks "Why?", it expands to include the context and rationale behind the decision.

Quick Start

Use the mnemonic progressive disclosure skill to recall information about the primary database.

Frequently Asked Questions about mnemonic-progressive-disclosure

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

FAQPage Schema
How does progressive disclosure for memory recall work?

Progressive disclosure for memory recall works by retrieving information in tiered levels: Quick Answer, Context, and Comprehensive Detail. This mechanism dynamically adjusts the output detail based on user intent, preventing information overload during retrieval.

How do I retrieve detailed context from a filesystem-based memory system without information overload?

To retrieve context without information overload, query the filesystem-based memory system with your specific intent. The system provides a Quick Answer initially, then expands to Context and Comprehensive Detail only if your follow-up questions require deeper implementation or debugging rationale.

When do I need tiered information retrieval for memory context?

You need tiered information retrieval when recalling memory for tasks ranging from simple factual queries to in-depth debugging. It ensures you receive the exact contextual relevance required, preventing excessive detail when you only need a concise factual answer.

Can I dynamically adjust the detail level of recalled memory during an active conversation?

Yes, you can dynamically adjust the detail level of recalled memory. The system supports contextually relevant expansion, allowing you to start with a Quick Answer and progressively request Context or Comprehensive Detail as your implementation or debugging needs evolve.

What is the best way to manage memory recall precision for implementation tasks?

The best way to manage memory recall precision is using a tiered progressive disclosure approach. It automatically determines the appropriate detail level based on your intent, delivering Comprehensive Detail for implementation tasks while keeping simple queries concise.