mcpbeat

Understand Explain

egonex-ai/understand-explain

Use when you need a deep-dive explanation of a specific file, function, or module in the codebase

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
77339
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/Egonex-AI/Understand-Anything --skill understand-explain

The instruction itself

4 sections, as written by the author

/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

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

Instructions

  • 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 $UA_DIR/knowledge-graph.json exists. If not, tell the user to run /understand first.
  • Check graph freshness before using graph-derived context:
  • Read project.gitCommitHash from the graph metadata as GRAPH_COMMIT_RAW. Resolve it as a commit before using it in any Git diff, then compare it with git rev-parse HEAD and inspect project-scoped committed and working-tree changes from the project root:
     GRAPH_COMMIT=$(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.
  • 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
  • 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
  • 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.
  • 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.
  • Read the actual source file — Read the source file at the node's filePath for the deep-dive analysis.
  • 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 understanding

How to use it

Copy the folder

Take egonex-ai/understand-explain from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.