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- .woodpecker.yaml: image paths -> library/autojanet-{agent,dispatcher}
- .woodpecker.yaml: secret names RS_HARBOR_USER / RS_HARBOR_PASS (global)
- container/Dockerfile: restore COPY skills/, skills/ populated from opencode config
- skills/: 84 opencode skills bundled into image
- k8s/manifests: update image refs to library/
55 lines
2.9 KiB
Markdown
55 lines
2.9 KiB
Markdown
---
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name: understand-chat
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description: Use when you need to ask questions about a codebase or understand code using a knowledge graph
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argument-hint: [query]
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---
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# /understand-chat
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Answer questions about this codebase using the knowledge graph at `.understand-anything/knowledge-graph.json`.
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## Graph Structure Reference
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The knowledge graph JSON has this structure:
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- `project` — {name, description, languages, frameworks, analyzedAt, gitCommitHash}
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- `nodes[]` — each has {id, type, name, filePath?, summary, tags[], complexity, languageNotes?}
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- Code node types: file, function, class, module, concept
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- Non-code node types: config, document, service, table, endpoint, pipeline, schema, resource
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- Domain/knowledge node types: domain, flow, step, article, entity, topic, claim, source
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- IDs use the node type as prefix, e.g. `file:path`, `function:path:name`, `config:path`, `article:path`
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- `edges[]` — each has {source, target, type, direction, weight}
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- Key types: imports, contains, calls, depends_on, configures, documents, deploys, triggers, contains_flow, flow_step, related, cites
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- `layers[]` — each has {id, name, description, nodeIds[]}
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- `tour[]` — each has {order, title, description, nodeIds[]}
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## How to Read Efficiently
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1. Use Grep to search within the JSON for relevant entries BEFORE reading the full file
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2. Only read sections you need — don't dump the entire graph into context
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3. Node names and summaries are the most useful fields for understanding
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4. Edges tell you how components connect — follow imports and calls for dependency chains
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## Instructions
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1. Check that `.understand-anything/knowledge-graph.json` exists in the current project root. If not, tell the user to run `/understand` first.
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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).
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3. **Search for relevant nodes** — use Grep to search the knowledge graph file for the user's query keywords: "$ARGUMENTS"
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- Search `"name"` fields: `grep -i "query_keyword"` in the graph file
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- Search `"summary"` fields for semantic matches
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- Search `"tags"` arrays for topic matches
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- Note the `id` values of all matching nodes
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4. **Find connected edges** — for each matched node ID, Grep for that ID in the `edges` section to find:
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- What it imports or depends on (downstream)
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- What calls or imports it (upstream)
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- This gives you the 1-hop subgraph around the query
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5. **Read layer context** — Grep for `"layers"` to understand which architectural layers the matched nodes belong to.
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6. **Answer the query** using only the relevant subgraph:
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- Reference specific files, functions, and relationships from the graph
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- Explain which layer(s) are relevant and why
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- Be concise but thorough — link concepts to actual code locations
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- If the query doesn't match any nodes, say so and suggest related terms from the graph
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