What problem does it solve?
When starting a new task or switching context, AI coding assistants lack awareness of past project decisions, coding conventions, and known pitfalls. This Skill pre-fetches relevant memories from mem0 so the assistant begins work with the right historical context already loaded.
Core Features & Use Cases
- Parallel Memory Search: Runs 2-4 parallel search_memories queries across different angles such as feature names, file paths, and error keywords, filtered by user and app IDs.
- Typed Memory Retrieval: Targets specific memory types including decisions, conventions, and anti-patterns to surface architecture choices, coding patterns, and known pitfalls.
- Compact Context Output: Deduplicates results by memory ID and outputs a plain-text block of at most 10 memories, staying silent when nothing relevant exists.
- Use Case: A developer starts work on a payment module; the Skill searches mem0 for prior decisions and conventions about that module and injects a concise summary before any code is written.
Quick Start
Ask the assistant to load context for the feature you are about to work on, for example by saying "load context for the authentication module".