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memory-recall

Search and retrieve historical project context via semantic vector queries.

1|Updated May 21, 2026
Search Tags:#context#recall#search#retrieval#memory#knowledge
One-click install
npx skills add https://github.com/hiddink-ai/hiddink-harness --skill memory-recall-hiddink-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-recall
Source: https://github.com/hiddink-ai/hiddink-harness/tree/main/templates/skills/memory-recall
Command: npx skills add https://github.com/hiddink-ai/hiddink-harness --skill memory-recall-hiddink-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of information fragmentation by allowing agents to perform semantic searches across historical project memories, ensuring that past decisions and context are never lost.

Core Features & Use Cases

  • Semantic Search: Uses vector-based retrieval to find relevant memories even when exact keywords are unknown.
  • Temporal Filtering: Allows users to narrow down results by date to find information from specific project phases.
  • Use Case: Quickly recall the rationale behind a specific architectural decision made months ago by searching for the relevant topic and date range.

Quick Start

Ask the system to recall memories related to the authentication flow by typing a natural language request for recent memory entries.

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 context and past decisions using semantic search?▼

Semantic search retrieves historical project context using vector-based queries to find relevant memories even when exact keywords are unknown. It searches the claude-mem storage system to ensure past decisions and rationale are never lost.

Can I filter retrieved project memories by a specific date or development phase?▼

Temporal filtering allows you to narrow down retrieved project memories by specific dates. This helps locate context and historical decisions from specific project phases, improving debugging and knowledge management accuracy.

How do I recall the rationale behind an architectural decision made months ago?▼

To recall architectural rationale, use a natural language request to search historical project memories. The vector-based retrieval matches relevant context even without exact keywords, bringing up past decisions and their reasoning.

Does semantic vector search for project memory require a specific storage interface?▼

Semantic vector search requires integration with the underlying chroma_query_documents interface. This interface performs the accurate information retrieval needed to search the claude-mem storage system for historical project context.

What is the best way to search project history when I do not know the exact keywords?▼

Vector-based semantic search is the best way to find project history without exact keywords. It uses semantic vector queries to retrieve relevant memories and historical context based on meaning and relevance rather than exact matches.

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