shine-vector-search

Name: shine-vector-search
Availability: InStock
Author: diShine

Store and recall semantic memories with metadata using Qdrant MCP.

1|Updated Apr 15, 2026

Search Tags:#store #recall #MCP #memory #semantic #Qdrant #vector-search

Ready to run

npx skills add https://github.com/diShine-digital-agency/SHINE-Code-System --skill shine-vector-search

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shine-vector-search
Source: https://github.com/diShine-digital-agency/SHINE-Code-System/tree/main/skills/shine-vector-search
Command: npx skills add https://github.com/diShine-digital-agency/SHINE-Code-System --skill shine-vector-search

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Storing and recalling semantic memories across sessions can be challenging; this skill provides a vector-based memory store that preserves context for cross-session knowledge continuity.

Core Features & Use Cases

Store mode: collect session findings and metadata, generate embeddings, and persist them in Qdrant MCP.
Recall mode: search embeddings to retrieve top matches with relevance scores, timestamps, and sources.
Use Case: maintain institutional memory across projects to inform proposals, onboarding, and knowledge transfer.

Quick Start

Store a topic from your current session to begin building cross-session semantic memory with Qdrant MCP.

Frequently Asked Questions about shine-vector-search

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

FAQPage Schema

How do I store and recall semantic memories across different AI sessions?▼

To store and recall semantic memories across sessions, you can use a vector-based memory system like Qdrant MCP. It preserves context by generating embeddings from session findings and retrieving semantically similar past entries to maintain cross-session knowledge continuity.

How does vector search retrieve past conversation context from a Qdrant MCP store?▼

Vector search retrieves past conversation context by matching query embeddings against stored vectors in Qdrant MCP. It returns top matches with relevance scores, timestamps, and source attributions to ensure accurate knowledge continuity across sessions.

Can I use vector search to maintain institutional memory for project onboarding and knowledge transfer?▼

Yes, you can use vector search to maintain institutional memory for onboarding and knowledge transfer. By storing session insights as semantic memories, the system retrieves relevant past entries across projects to inform proposals and facilitate knowledge continuity.

Does this semantic memory skill provide guardrails to prevent storing personally identifiable information?▼

Yes, the semantic memory skill provides guardrails to prevent PII storage. It ensures source attribution and provenance when persisting session insights and metadata into the Qdrant MCP vector store.

What is the best way to build long-term memory in AI-assisted workflows using a vector store?▼

The best way to build long-term memory in AI-assisted workflows is using a Qdrant MCP vector store to persist session findings as embeddings. This enables semantic recall of past entries, ensuring knowledge management and context continuity across conversations.

What are the limitations of using a vector store for cross-session semantic memory?▼

Limitations of using a vector store for cross-session semantic memory include dependency on Qdrant MCP integration and the need for guardrails to prevent PII storage. Retrieval relies on semantic similarity, which requires accurate embeddings to maintain context.