beacon-chat

name	beacon-chat
description	Use when you need to ask questions about a codebase or understand code using a knowledge graph
argument-hint	["query"]

/beacon-chat

Answer questions about this codebase using the knowledge graph at .beacon/knowledge-graph.json.

Graph Structure Reference

The knowledge graph JSON has this structure:

• project — {name, description, languages, frameworks, analyzedAt, gitCommitHash}
• nodes[] — each has {id, type, name, filePath?, summary, tags[], complexity, languageNotes?}
  • Code node types: file, function, class, module, concept
  • Non-code node types: config, document, service, table, endpoint, pipeline, schema, resource
  • Domain/knowledge node types: domain, flow, step, article, entity, topic, claim, source
  • IDs use the node type as prefix, e.g. file:path, function:path:name, config:path, article:path
• edges[] — each has {source, target, type, direction, weight}
  • Key types: imports, contains, calls, depends_on, configures, documents, deploys, triggers, contains_flow, flow_step, related, cites
• layers[] — each has {id, name, description, nodeIds[]}
• tour[] — each has {order, title, description, nodeIds[]}

How to Read Efficiently

1. Use Grep to search within the JSON for relevant entries BEFORE reading the full file
2. Only read sections you need — don't dump the entire graph into context
3. Node names and summaries are the most useful fields for understanding
4. Edges tell you how components connect — follow imports and calls for dependency chains

Instructions

1. Check that .beacon/knowledge-graph.json exists in the current project root. If not, tell the user to run /beacon first.

2. Read project metadata only — use Grep or Read with a line limit to extract just the "project" section from the top of the file for context (name, description, languages, frameworks).

3. Search for relevant nodes — use Grep to search the knowledge graph file for the user's query keywords: "$ARGUMENTS"

   • Search "name" fields: grep -i "query_keyword" in the graph file
   • Search "summary" fields for semantic matches
   • Search "tags" arrays for topic matches
   • Note the id values of all matching nodes

4. Find connected edges — for each matched node ID, Grep for that ID in the edges section to find:

   • What it imports or depends on (downstream)
   • What calls or imports it (upstream)
   • This gives you the 1-hop subgraph around the query

5. Read layer context — Grep for "layers" to understand which architectural layers the matched nodes belong to.

6. Answer the query using only the relevant subgraph:

   • Reference specific files, functions, and relationships from the graph
   • Explain which layer(s) are relevant and why
   • Be concise but thorough — link concepts to actual code locations
   • If the query doesn't match any nodes, say so and suggest related terms from the graph