mcp-knowledge-graph

Stores and queries persistent codebase architecture facts in a knowledge graph via MCP memory tools.

2|3|Updated Mar 13, 2026
One-click install
npx skills add https://github.com/spideynolove/claude-dotfiles --skill mcp-knowledge-graph-spideynolove
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mcp-knowledge-graph
Source: https://github.com/spideynolove/claude-dotfiles/tree/main/.agents-global/skills.disabled/mcp-knowledge-graph
Command: npx skills add https://github.com/spideynolove/claude-dotfiles --skill mcp-knowledge-graph-spideynolove

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Re-reading the same files every session wastes context and time. This Skill persists architectural facts about a codebase—components, dependencies, and decisions—into a queryable knowledge graph so you can recall project knowledge across sessions instead of re-analyzing the repository. ## Core Features & Use Cases - Persistent Entity Storage: Create typed entities (services, repositories, modules, decisions) with aim_memory_store and append observations with aim_memory_add_facts. - Relationship Mapping: Link entities with semantic relations like depends_on, calls, and implements using aim_memory_link. - Flexible Storage Locations: Choose project-local .aim/memory.jsonl storage for team knowledge or global stores for personal notes. - Use Case: After running repomix on a new repository, extract the key services and their dependencies, store them in the project graph, then query them with aim_memory_search in future sessions instead of re-reading source files. ## Quick Start Analyze this repository and store its main components, dependencies, and architectural decisions in the project knowledge graph for future sessions.

Frequently Asked Questions about mcp-knowledge-graph

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

FAQPage Schema
How do I remember codebase architecture across AI sessions?

Store architectural facts as entities with aim_memory_store and link them with aim_memory_link after analyzing the codebase. In later sessions, query the graph with aim_memory_search or aim_memory_get instead of re-reading source files.

How to build a knowledge graph from a code repository?

Gather broad context first, often with repomix, then extract durable facts rather than raw code. Create entities with semantic types like service or module, and connect them using active relation verbs such as depends_on, calls, and implements.

Where is the knowledge graph data stored?

With location set to project, data is written to .aim/memory.jsonl inside the repository, making it easy to commit and share. Setting location to global stores it in a personal global memory file, optionally under a named context.

Does aim_memory_search support semantic search?

No, aim_memory_search is substring-based, not semantic, and aim_memory_get requires exact entity names. Use predictable terminology and stable entity names like AuthService so keyword lookups reliably match stored observations.

When should I not use a persistent knowledge graph?

Avoid it for temporary session notes, raw code dumps, or general chat memory. Writes rewrite the whole storage file, so prefer focused graphs over huge ones to keep operations efficient.