Arch Diagram

/arch-diagram

Generate an architecture diagram from RNA data. Teaches the traversal — the user picks the output format.

When to Use

When the user asks for an architecture diagram, system overview, service map, or component diagram of a repo that has an RNA index.

RNA Query Sequence

Execute these in order. Each step builds on the previous.

Step 1: Orientation (entry points, hotspots, outcomes)

repo_map(repo="<path>")

Entry points reveal deployment units (each entry point = a runnable service/script). Hotspot files reveal where complexity concentrates. Active outcomes reveal what the system is for.

Step 2: API Surface

search(query="", kind="api_endpoint", repo="<path>", limit=30, compact=true)

API endpoints reveal the contract between services. Group by file path to identify which deployment unit owns which routes.

Step 3: Frameworks (Infrastructure Stack)

search(query="", kind="framework", repo="<path>", limit=30, compact=true)

Each framework node has a language context. Group them: "this is a C#/ASP.NET service with npgsql" vs "this is a TypeScript/React frontend." LLMs know what each framework does — use that knowledge to identify infrastructure dependencies (databases, caches, message brokers, external APIs).

Step 4: Workspace and Languages

list_roots(repo="<path>")

Extract: root names, languages detected, framework counts. This confirms the language mix and workspace boundaries.

Step 5: Business Context

outcome_progress(repo="<path>")

If outcomes are declared, this reveals what the system is trying to achieve — annotate the diagram with business purpose, not just technical structure.

Step 6: External Service Calls (Optional — for deeper diagrams)

search(query="https://api.", repo="<path>", limit=20)
search(query="connection", kind="const", repo="<path>", limit=10)

String literal constants containing API URLs and connection strings reveal external dependencies and can distinguish different uses of the same service (e.g., OpenAI completions endpoint vs embeddings endpoint).

Inferring Architecture

Deployment Units

Group by: entry points + directory prefixes + project files (.csproj, package.json, Cargo.toml). Each entry point from repo_map typically represents a separate deployable. Common patterns:

• src/Services/X/ + entry point = a service
• apps/web/ + React framework = SPA frontend
• src/Workers/X/ + entry point = background worker
• scripts/ = operational tooling (usually not a deployed service)

Component Annotations

Every component should show:

• Language (from the nodes' language field)
• Primary framework (from framework detection)
• Example: "Northwoods.Api (C# / ASP.NET)" not just "API"

Edges Between Components

• Frontend → API: inferred from framework combo (React + ASP.NET = SPA calling API)
• Service → Database: inferred from database frameworks (npgsql, mongoose, sqlalchemy)
• Service → External API: inferred from URL constants (Step 6)
• Service → Object Store: inferred from storage frameworks (minio, boto3, aws-sdk)

Infrastructure Nodes

Databases, caches, message brokers, and external APIs are infrastructure — render them differently (cylinders for storage, clouds for external APIs). The framework names tell you what they are.

Output

Produce the diagram in whatever format the user requests (d2, mermaid, plantuml, ASCII, etc.). If the user doesn't specify, ask.

The diagram should show:

1. Deployment units with language + framework labels
2. Infrastructure (databases, caches, queues, external APIs)
3. Edges with protocol/method annotations (HTTP, SQL, gRPC, etc.)
4. Business context from outcomes (what the system does, not just what it's built with)

Keep it high-level — this is architecture, not a class diagram. Collapse internal details. One box per deployable, not one box per file.