memory-retrieve

Classify user queries to retrieve memories via recent, keyword, or semantic search.

Updated Jan 15, 2026

Search Tags:#API #retrieval #memory #classification #bun #vector-search #keyword-search

Ready to run

npx skills add https://github.com/whiteboardev/brainy.chat --skill memory-retrieve

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-retrieve
Source: https://github.com/whiteboardev/brainy.chat/tree/main/.opencode/skills/memory-retrieve
Command: npx skills add https://github.com/whiteboardev/brainy.chat --skill memory-retrieve

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines memory access by automatically classifying your query and selecting the most effective retrieval method, reducing manual searching and context-switching.

Core Features & Use Cases

Adaptive retrieval: routes queries to recent, keyword, or semantic memory searches based on analysis.
Fallback handling: seamlessly falls back to keyword search when vector results are unavailable, ensuring reliable results.
Operational transparency: reports the chosen method and token estimates to support auditing and optimization.

Quick Start

To retrieve memories for a project, run the main script with a project ID and an optional query, for example: bun .opencode/skills/memory-retrieve/scripts/retrieve-memories.ts PROJECT_ID [QUERY] [OPTIONS]

Frequently Asked Questions about memory-retrieve

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

FAQPage Schema

How does adaptive memory retrieval classify queries to find relevant context?▼

Adaptive memory retrieval classifies user queries and routes them to recent, keyword, or semantic search strategies. It automatically selects the most effective retrieval method to reduce manual searching and context-switching.

How do I retrieve memories for a project using a local script?▼

To retrieve memories, run the main script with a project ID and optional query: `bun .opencode/skills/memory-retrieve/scripts/retrieve-memories.ts PROJECT_ID [QUERY] [OPTIONS]`. This interfaces with local scripts and API endpoints to execute searches.

What happens when vector search yields no results during memory classification?▼

When vector search yields no results, the system implements fallback handling by automatically switching to keyword search. This ensures reliable memory retrieval even when semantic search methods return empty results.

Does the memory retrieval process expose token estimates and method metadata for auditing?▼

Yes, the memory retrieval process provides operational transparency by reporting the chosen method and token estimates. This supports auditing and optimization for downstream tools consuming the API endpoints.

What is the best way to combine keyword and semantic search for memory access?▼

The best way to combine these methods is using a classification system that routes queries to keyword or semantic retrieval adaptively. It falls back to keyword search when vector results are unavailable, ensuring reliable access.