Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.
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Entity and relation tracking skill using MCP memory server patterns and local knowledge graph storage. Build persistent knowledge graphs of entities, relationships, and observations across agent sessions. Covers the MCP memory server (@modelcontextprotocol/server-memory), local JSON-based graphs, and entity-relation querying patterns for long-running agents.
version
1.0.0
model
sonnet
invoked_by
both
user_invocable
true
tools
["Bash","Read","Write","Edit","Glob","Grep"]
best_practices
["Always use entity IDs (not names) as stable references","Store observations with timestamps for temporal reasoning","Normalize entity types across all insertions","Use relation types that are directional (e.g., \"manages\" not \"related_to\")","Prune stale observations periodically to keep graph queryable"]
error_handling
graceful
streaming
not_applicable
verified
true
lastVerifiedAt
"2026-03-15T00:00:00.000Z"
source
builtin
trust_score
100
provenance_sha
999cb8a134ef1c94
Knowledge Graph
Overview
Build and query persistent knowledge graphs of entities, relationships, and observations across agent sessions. Enables agents to accumulate structured knowledge over time — tracking what they've learned about projects, people, codebases, and domains.
When to Invoke
Skill({ skill: 'knowledge-graph' }) when:
Agent needs to remember structured facts across sessions
Building a model of a codebase, project, or domain from observations
Tracking relationships between people, systems, or concepts
Implementing long-term memory that survives context resets
Option 1: MCP Memory Server (Recommended for Claude Agents)
The official MCP memory server provides a persistent knowledge graph accessible via tool calls.
# Check what the agent already knows
mcp__memory__search_nodes({ query: "<task domain>" })
After completing work:
// Record key discoveries as observationsmcp__memory__add_observations({
observations: [
{
entityName: '<project-or-domain>',
contents: ['<what was learned this session>'],
},
],
});
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.