Individual skill
knowledge-graphs
Build and query knowledge graphs — entity extraction, relation modeling, graph databases, and graph-based reasoning. Use when relationships between entities matter as much as the entities themselves.
skills/knowledge-graphs/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 54
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-agents-automation Repository topic
- ai-skills Repository topic
- awesome Repository topic
- awesome-list Repository topic
- chatgpt Repository topic
- claude-skills Repository topic
- meta-ai Repository topic
- muse-ai Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Read excerpt
# Knowledge Graphs
A knowledge graph stores the world as entities and relationships: people, places, concepts
connected by typed edges. It answers questions that tables and vectors struggle with — multi-hop
relationships, structured constraints, and "how are these connected?"
## Overview
Building one: extract entities and relations from text (or model them from structured data),
resolve duplicates (entity linking), store in a graph database, and query with graph patterns. The
power is in traversal — following relationships across hops — and in combining graph structure
with vector similarity for hybrid retrieval. The cost is modeling effort: the schema (ontology)
must be designed, and extraction quality bounds everything.
