understand-explain
The understand-explain Claude Code skill performs deep-dive analysis of specific code components by querying a knowledge graph JSON file. Use it when you need comprehensive explanations of files, functions, modules, or classes within a codebase that has already been analyzed by the understand-anything tool. It efficiently locates target nodes, traces their dependencies and connections, and synthesizes surrounding component context to provide thorough documentation.
git clone --depth 1 https://github.com/Egonex-AI/Understand-Anything /tmp/understand-explain && cp -r /tmp/understand-explain/understand-anything-plugin/skills/understand-explain ~/.claude/skills/understand-explainSKILL.md
# /understand-explain
Provide a thorough, in-depth explanation of a specific code component.
## 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. **Resolve the data directory `$UA_DIR`.** Run `UA_DIR=$([ -d .understand-anything ] && echo .understand-anything || echo .ua)` — this is the legacy `.understand-anything/` when it already exists, otherwise the new `.ua/`. Check that `$UA_DIR/knowledge-graph.json` exists. If not, tell the user to run `/understand` first.
2. **Check graph freshness before using graph-derived context**:
- Read `project.gitCommitHash` from the graph metadata as `GRAPH_COMMIT_RAW`. Resolve it as a commit before using it in any Git diff, then compare it with `git rev-parse HEAD` and inspect project-scoped committed and working-tree changes from the project root:
```bash
GRAPH_COMMIT=$(git rev-parse --verify --end-of-options "${GRAPH_COMMIT_RAW}^{commit}" 2>/dev/null)
git rev-parse HEAD
git diff --name-only "$GRAPH_COMMIT" HEAD -- .
git diff --cached --name-only -- .
git diff --name-only -- .
git ls-files --others --exclude-standard -- .
```
- The `-- .` pathspec is required: commits that only touch a sibling monorepo project must not make this graph stale. A hash mismatch alone is not stale when the project diff is empty.
- Ignore the selected data directory (`.ua/` or legacy `.understand-anything/`) in every command's output because it contains generated graph artifacts, not project source drift.
- If the committed diff or any working-tree command reports project files, warn before explaining that graph-derived context may omit those changes. Suggest: Run `/understand` to refresh the graph.
- Run the commit diff only when `GRAPH_COMMIT_RAW` resolves successfully. If the graph commit or Git metadata is missing, invalid, or unavailable, give a brief best-effort warning and continue instead of blocking.
3. **Find the target node** — use Grep to search the knowledge graph for the component: "$ARGUMENTS"
- For file paths (e.g., `src/auth/login.ts`): search for `"filePath"` matches
- For function notation (e.g., `src/auth/login.ts:verifyToken`): search for the function name in `"name"` fields filtered by the file path
- Note the exact node `id`, `type`, `summary`, `tags`, and `complexity`
4. **Find all connected edges** — Grep for the target node's ID in the edges section:
- `"source"` matches → things this node calls/imports/depends on (outgoing)
- `"target"` matches → things that call/import/depend on this node (incoming)
- Note the connected node IDs and edge types
5. **Read connected nodes** — for each connected node ID from step 4, Grep for those IDs in the nodes section to get their `name`, `summary`, and `type`. This builds the component's neighborhood.
6. **Identify the layer** — Grep for the target node's ID in the `"layers"` section to find which architectural layer it belongs to and that layer's description.
7. **Read the actual source file** — Read the source file at the node's `filePath` for the deep-dive analysis.
8. **Explain the component in context**:
- Its role in the architecture (which layer, why it exists)
- Internal structure (functions, classes it contains — from `contains` edges)
- External connections (what it imports, what calls it, what it depends on — from edges)
- Data flow (inputs → processing → outputs — from source code)
- Explain clearly, assuming the reader may not know the programming language
- Highlight any patterns, idioms, or complexity worth understandingUse when you need to ask questions about a codebase or understand code using a knowledge graph
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