research-patterns

Name: research-patterns
Availability: InStock
Author: mindmorass

Queries Qdrant stores for knowledge and augments with web searches.

2|Updated Jan 2, 2026

Search Tags:#knowledge retrieval #web search #RAG #citation #Qdrant #data harvesting

Ready to run

npx skills add https://github.com/mindmorass/reflex --skill research-patterns-mindmorass

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-patterns
Source: https://github.com/mindmorass/reflex/tree/main/plugins/reflex/skills/research-patterns
Command: npx skills add https://github.com/mindmorass/reflex --skill research-patterns-mindmorass

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves knowledge retrieval and synthesis from stored knowledge sources using a vector store.

Core Features & Use Cases

Qdrant-backed retrieval: Query and prioritize stored knowledge before performing external research.
Citation tracking: Attach sources and metadata to harvested results for auditing and reuse.
External research augmentation: Seamlessly augment stored results with trusted external sources when gaps exist.
Use Case: A researcher consolidates internal documents and web sources into a coherent summary with citations.

Quick Start

Use the research-patterns skill to query the Qdrant store for a topic and, if needed, perform external web searches to augment results and generate citations.

Frequently Asked Questions about research-patterns

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

FAQPage Schema

How do I retrieve knowledge from a vector store using semantic search?▼

Knowledge retrieval from a vector store queries stored embeddings to find relevant documents by semantic similarity. This Skill uses Qdrant to prioritize matching results from your internal knowledge base before performing external searches, enabling fast, contextual answers grounded in your own sources.

Can I combine internal documents with web search results in a single research output?▼

Yes. This Skill augments Qdrant-backed retrieval with external web search when stored results have gaps, then synthesizes both into a coherent summary. It tracks citations and metadata from each source so you know where every piece of information originated.

How do I ensure research results are properly cited and traceable?▼

Citation tracking is built into this Skill's retrieval pipeline. It attaches source metadata and references to all harvested results—both from Qdrant storage and external research—so you can audit provenance and reuse sources across projects.

What's the best way to structure internal documents for vector-based research?▼

Store documents in Qdrant with meaningful metadata and embeddings so semantic search can retrieve them efficiently. This Skill enforces best practices for metadata storage and freshness checks, ensuring your knowledge base stays accurate and queryable.

When should I use a vector store instead of keyword search for research?▼

Vector stores excel when you need semantic understanding—finding conceptually related documents even if keywords don't match exactly. Use this approach for synthesizing knowledge across diverse sources where meaning matters more than keyword overlap.

Can this Skill handle research tasks that need both stored and real-time information?▼

Yes. This Skill queries Qdrant first for stored knowledge, then seamlessly integrates fresh external web search results when needed. This hybrid approach balances fast retrieval from your knowledge base with current information from the web.