rysweet/agent-kgpacks/ladybugdb-expert

Claude Code and agent skill by rysweet/agent-kgpacks

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Comprehensive knowledge of LadybugDB, the active community fork of the archived Kuzu graph database. Covers the Python SDK (real_ladybug package), Cypher query language, vector search extensions, full-text search, graph algorithms, import/export, and migration from Kuzu.

Install

npx vskill@latest install rysweet/agent-kgpacks/ladybugdb-expert

SKILL.md

Ladybugdb Expert Skill

Knowledge graph: 43 articles, 207 entities, 178 relationships

Overview

Comprehensive knowledge of LadybugDB, the active community fork of the archived Kuzu graph database. Covers the Python SDK (real_ladybug package), Cypher query language, vector search extensions, full-text search, graph algorithms, import/export, and migration from Kuzu.

Usage

This skill provides deep domain expertise through a knowledge graph. Ask questions naturally and the skill will retrieve relevant information from the graph database.

Example queries:

  • "Explain ladybugdb concepts"
  • "What is the relationship between X and Y in ladybugdb?"
  • "Find articles about [topic] in ladybugdb"

Knowledge Domains

This pack covers 43 articles across multiple domains within ladybugdb.

Quality Metrics:

  • Accuracy: 0.0%
  • Hallucination Rate: 0.0%
  • Citation Quality: 0.0%

Technical Details

  • Database: Kuzu graph database (2.08 MB)
  • Configuration: data/packs/ladybugdb-expert/kg_config.json
  • License: MIT

Sources

This knowledge pack was created from:

  • https://docs.ladybugdb.com/
  • https://docs.ladybugdb.com/client-apis/python/
  • https://docs.ladybugdb.com/extensions/vector/
  • https://github.com/LadybugDB/ladybug

Integration

This skill integrates with WikiGR's KG Agent for semantic retrieval.

How to Use This Pack:

When a user asks a question related to this domain, automatically:

python
from wikigr.agent.kg_agent import KnowledgeGraphAgent
from pathlib import Path
# Initialize KG Agent with pack database
pack_db = Path("data/packs/ladybugdb-expert/pack.db").resolve()
with KnowledgeGraphAgent(db_path=str(pack_db), read_only=True) as kg_agent:
result = kg_agent.query(user_question, max_results=5)
# Use result["answer"] and result["sources"] in your response

Retrieval Strategy:

  1. Vector Search: Find semantically similar content
  2. Graph Traversal: Navigate relationships between entities
  3. Hybrid Search: Combine vector similarity with graph structure

Created: 2026-03-03T18:44:21.748608Z

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

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Source

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