memory

Search archived project context by keyword and load specific entries.

8|Updated May 18, 2026

Search Tags:#memory #historical context #project decisions #architecture records #archive search #lesson retrieval

Ready to run

npx skills add https://github.com/lalulali/vespyr --skill memory-lalulali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/lalulali/vespyr/tree/main/.agents/skills/memory
Command: npx skills add https://github.com/lalulali/vespyr --skill memory-lalulali

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you find historical project context that has been compacted out of active memory, so you can recover past decisions, lessons, and patterns without digging through old files manually.

Core Features & Use Cases

Archive search: Look up prior decisions, architecture choices, and lessons by keyword.
Context recovery: Load a specific archived entry when a search result points to the right historical record.
Workflow support: Use it when asking questions like what auth approach was chosen, what performance lessons were learned, or where a past architecture decision was documented.

Quick Start

Ask the memory skill to search for the relevant historical decision or lesson in the project archive.

Frequently Asked Questions about memory

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

FAQPage Schema

How do I recover past architecture decisions that have been compacted out of active memory?▼

To recover past architecture decisions, use keyword-based archive search to retrieve compacted historical project context. The system applies relevance ranking to match your query with archived entries, allowing you to locate specific architecture records without manual file digging.

What is the best way to find historical project context and lessons learned?▼

The best way to find historical project context is performing a keyword-based archive search with relevance ranking. This retrieves compacted lessons and patterns from past engineering workflows, helping you identify and optionally load the specific archived entries you need.

How does keyword-based archive search work for retrieving old project records?▼

Keyword-based archive search works by querying an index of compacted historical context and applying relevance ranking to the results. Once the search identifies matching historical records, you can optionally load the specific archived entry to review the full decision or lesson.

Can I retrieve specific architecture records if I only remember a few keywords?▼

Yes, you can retrieve specific architecture records using just a few keywords. The archive search applies relevance ranking to match your keywords against compacted historical context, returning ranked results that point you to the exact archived entry for optional loading.

Does this memory skill support retrieving lessons across both product and engineering workflows?▼

Yes, this skill supports retrieving lessons across both product and engineering workflows. It applies keyword-based archive search to recover historical project context, including past decisions, lessons, and patterns documented throughout the project lifecycle.

What are the limitations of relying on compacted historical project context for decision recovery?▼

A limitation of relying on compacted historical context is that search results depend entirely on the keywords provided. If the archive index lacks the specific terms or if context was overly compacted, relevance ranking may fail to surface the correct historical record.