memory-recall

Retrieve and synthesize historical project decisions from markdown memory stores.

2|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/alex-voloshin-dev/ai-skills --skill memory-recall-alex-voloshin-dev
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: memory-recall
Source: https://github.com/alex-voloshin-dev/ai-skills/tree/main/plugin/skills/memory-recall
Command: npx skills add https://github.com/alex-voloshin-dev/ai-skills --skill memory-recall-alex-voloshin-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of fragmented project knowledge by surfacing historical decisions, technical conventions, and past bug fixes directly within the agent's workflow.

Core Features & Use Cases

  • Contextual Retrieval: Searches project-level (L4) and user-global (L5) memory stores for relevant insights.
  • Conflict Detection: Identifies and flags contradictory information between different memory layers to prevent incorrect assumptions.
  • Use Case: Before starting a new feature, use this skill to recall previous architectural decisions regarding authentication flows or error handling patterns to ensure consistency with existing code.

Quick Start

Use the memory-recall skill to search for all project memory entries related to authentication.

Frequently Asked Questions about memory-recall

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

FAQPage Schema
How do I retrieve historical project decisions and conventions for development tasks?

To retrieve historical project decisions, you query project-level and user-global markdown memory stores. This synthesizes past architectural choices and technical conventions to provide context-aware insights for your current development workflow.

What is the best way to manage fragmented project knowledge and surface past bug fixes?

Managing fragmented project knowledge involves searching defined memory layers to surface past bug fixes and technical conventions directly. This ensures historical decisions are accessible and synthesized within the development environment.

How does conflict resolution work when retrieving contradictory information from memory layers?

Conflict resolution identifies and flags contradictory information discovered between different memory layers during retrieval. This mechanism prevents incorrect assumptions by highlighting conflicting historical decisions before they are injected into the workflow.

Can I use markdown-based memory stores to maintain context for new feature development?

Yes, you can use markdown-based memory stores to maintain context for new features. Searching these stores recalls previous architectural decisions regarding patterns like authentication flows, ensuring consistency with existing code.

How do you ensure secure information injection when retrieving untrusted content from memory?

Secure information injection is ensured through strict provenance tracking and untrusted content wrapping. This process securely manages historical project data retrieved from memory layers before it reaches the development agent.

When do I need provenance tracking for knowledge management in software engineering?

You need provenance tracking when retrieving historical project decisions to guarantee reliable information injection. It traces the origin of synthesized insights from memory stores, ensuring development tasks use verified and secure context.