understand-explain
egonex-ai/understand-anything
Deep-dive explanations of code components using AI-powered knowledge graphs.
What is understand-explain?
Generates thorough, in-depth explanations of specific files, functions, or modules in your codebase by analyzing a knowledge graph. Use this when you need to understand how a component works, its role in the architecture, and how it connects to other parts of the system.
- Resolves target code components (files, functions, classes, modules) from the knowledge graph
- Traces all incoming and outgoing connections (imports, calls, dependencies) to map the component's neighborhood
- Identifies the architectural layer and role of the component within the system
- Analyzes internal structure and data flow from source code
- Detects graph staleness by comparing commit hashes and warns if the codebase has changed since analysis
How to install understand-explain
npx skills add https://github.com/egonex-ai/understand-anything --skill understand-explain- Knowledge graph must exist at `.ua/knowledge-graph.json` or `.understand-anything/knowledge-graph.json`
- Run `/understand` first to generate the knowledge graph if it does not exist
- Git repository with commit history (for staleness detection)
How to use understand-explain
- 1.Run the skill with a file path, function notation, or module name as the argument (e.g., `src/auth/login.ts` or `src/auth/login.ts:verifyToken`)
- 2.The skill resolves the data directory and checks if the knowledge graph exists
- 3.It verifies graph freshness by comparing the graph's commit hash with the current HEAD and detecting any project-scoped changes
- 4.The skill searches the graph for the target node and traces all connected edges to build the component's neighborhood
- 5.It reads the actual source file and generates a detailed explanation covering role, structure, connections, and data flow
- 6.If the graph is stale, a warning is issued suggesting you run `/understand` to refresh
Use cases
- Understanding the purpose and implementation of a critical function before modifying it
- Tracing how a module fits into the overall architecture and what depends on it
- Investigating a complex file to understand its internal structure and external connections
- Onboarding to a new codebase by exploring key components and their relationships
- Debugging by understanding the data flow and dependencies of a problematic component
- Developers onboarding to a new codebase
- Engineers investigating unfamiliar code before making changes
- Teams documenting architectural decisions and component responsibilities
- Code reviewers needing context on complex components
understand-explain FAQ
Run `/understand` first to analyze your codebase and generate the knowledge graph. This creates the `.ua/` directory with the required `knowledge-graph.json` file.
The skill compares the commit hash stored in the graph with your current Git HEAD. If your codebase has changed since the graph was generated, the explanation may omit recent changes. Run `/understand` to refresh the graph.
Yes. Use function notation like `src/auth/login.ts:verifyToken` to target a specific function. The skill will search the graph for that function name within the specified file.
Use a partial path or the component name. The skill uses Grep to search the knowledge graph, so it will find matches even with incomplete paths. If multiple matches exist, it will report them.
Yes. The knowledge graph is language-agnostic and supports any language your codebase uses. The skill explains components clearly regardless of the programming language.
Full instructions (SKILL.md)
Source of truth, from egonex-ai/understand-anything.
name: understand-explain description: Use when you need a deep-dive explanation of a specific file, function, or module in the codebase argument-hint: "[file-path]"
/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
$UA_DIR. RunUA_DIR=$([ -d .understand-anything ] && echo .understand-anything || echo .ua)— this is the legacy.understand-anything/when it already exists, otherwise the new.ua/. Check that$UA_DIR/knowledge-graph.jsonexists. If not, tell the user to run/understandfirst. -
Check graph freshness before using graph-derived context:
- Read
project.gitCommitHashfrom the graph metadata asGRAPH_COMMIT_RAW. Resolve it as a commit before using it in any Git diff, then compare it withgit rev-parse HEADand inspect project-scoped committed and working-tree changes from the project root:GRAPH_COMMIT=$(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
/understandto refresh the graph. - Run the commit diff only when
GRAPH_COMMIT_RAWresolves successfully. If the graph commit or Git metadata is missing, invalid, or unavailable, give a brief best-effort warning and continue instead of blocking.
- Read
-
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, andcomplexity
- For file paths (e.g.,
-
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, andtype. 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
filePathfor 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
containsedges) - 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
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