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architecture-knowledge-graph

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Guide for reading a codebase and building a high-quality, persistent architecture knowledge graph via the Excalidraw Architect MCP kg_* tools. Use when creating or updating the knowledge graph (.claude/architecture.md), mapping services and their dependencies, or analyzing one or more repos to capture how services communicate.

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/BV-Venky/excalidraw-architect-mcp/blob/HEAD/.skills/architecture-knowledge-graph/SKILL.md

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After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Architecture Knowledge Graph Guide

This skill teaches you how to turn a codebase into a clean, durable knowledge graph using the kg_* MCP tools. The graph (default .claude/architecture.md) is the single source of truth; diagrams are rendered views of it.

Graph quality depends almost entirely on how well you read the code. The MCP preserves, validates, and renders whatever you put in — it does not analyze the codebase. That analysis is your job.

Core Principle

Model services/components and how they communicate, not classes, functions, or every import. A "service" is a deployable unit / bounded context — one repo, one container, one logical responsibility.

Workflow (do the passes in order — don't interleave)

  1. kg_init once (or kg_import to bootstrap from an existing .excalidraw).
  2. Discovery pass — identify every service first. Add each with kg_add_service, setting component_type, domain, and owner while the info is in front of you (Dockerfiles, CODEOWNERS, READMEs, manifests).
  3. Linking pass — only after all services exist, add edges with kg_link. Linking before a target exists fails; that's why discovery comes first.
  4. kg_lint — catch dangling references, cycles, and single points of failure. Fix them before rendering.
  5. kg_render (whole graph) or kg_render_view / kg_render_around / kg_render_domain for focused diagrams.

How to find the edges (where services talk)

Look for these signals and translate each into a labelled kg_link:

Signal in codeLinkLabel / style
HTTP client, base URL, OpenAPI/Swagger specsync call"REST /orders", style: solid
gRPC stub / proto servicesync call"gRPC GetOrder", style: solid
Kafka/SQS/RabbitMQ producerasync event"Kafka order.created", style: dashed
Kafka/SQS/RabbitMQ consumerasync event (incoming)label with the same topic
DB connection string / ORM configdatastore"reads/writes", style: solid
Service-discovery / env var (PAYMENTS_URL)dependencyname the protocol

Cross-repo detection (multiple repos)

To connect services across repos, match a producer in repo A to a consumer in repo B using the shared join key:

  • Async: topic / queue name (A publishes order.created, B subscribes order.created → kg_link order-svc -> fulfilment-svc "Kafka order.created" style=dashed).
  • Sync: the called service's route/host (A calls https://payments/charge → link A → payments).

Do a producer/consumer inventory across all repos before linking, so you don't miss an edge whose two ends live in different codebases.

Conventions (keep the graph consistent across sessions)

  • Stable kebab-case ids, reused everywhere: order-service — never OrderService one session and orders the next. Id drift silently corrupts a cumulative graph.
  • Every edge is labelled with the mechanism. No bare arrows between services.
  • Style encodes transport: solid = sync (REST/gRPC/DB), dashed = async (events/queues). Consistency makes diagrams instantly readable.
  • Set domain to group services (team / bounded context / tier) so you can render one domain at a time.

Granularity

The full graph may be large, but any single rendered view should stay readable (~6–15 services). If a domain has 20 services, render it per sub-domain with kg_render_domain / kg_render_view rather than one giant picture. Resist decomposing a service into its internal classes — that's a different diagram.

Known limitation: parallel edges

A dependency is keyed on (from_id, to_id), so a second kg_link between the same pair replaces the first. If A talks to B two ways (REST and Kafka), combine them into one label — "REST /orders · Kafka order.created" — or link the primary transport. Don't issue two kg_link calls expecting two arrows.

Verify

Unlike diagrams, the graph is deterministically checkable. After building, run whats_connected_to <service> and kg_lint to sanity-check the topology matches your understanding of the system before sharing rendered views.