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understand-chat skill

by Egonex-AI·Egonex-AI/Understand-Anything·84k stars·MIT

Use when you need to ask questions about a codebase or understand code using a knowledge graph

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Install the understand-chat skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/Egonex-AI/Understand-Anything.git /tmp/Understand-Anything
mkdir -p ~/.claude/skills
cp -r /tmp/Understand-Anything/understand-anything-plugin/skills/understand-chat ~/.claude/skills/understand-chat
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

/understand-chat

Answer questions about this codebase using the knowledge graph in the project's data directory (.ua/knowledge-graph.json, or the legacy .understand-anything/knowledge-graph.json when that directory is present).

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, dependson, configures, documents, deploys, triggers, containsflow, 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 $UADIR. Run UADIR=$([ -d .understand-anything ] && echo .understand-anything || echo .ua) — this is the legacy .understand-anything/ when it already exists, otherwise the new .ua/. Check that $UA_DIR/knowledge-graph.json exists in the current project root. If not, tell the user to run /understand first.
  1. Check graph freshness before using graph-derived context:
  • Read project.gitCommitHash from the graph metadata as GRAPHCOMMITRAW. Resolve it as a commit before using it in any Git diff, then compare it with git rev-parse HEAD and inspect project-scoped committed and working-tree changes from the project root:
GRAPH_COMMIT=$(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 answering that graph-derived context may omit those changes. Suggest: Run /understand to refresh the graph.
  • Run the commit diff only when GRAPHCOMMITRAW 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.
  1. 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).
  1. 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
  1. 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
  1. Read layer context — Grep for "layers" to understand which architectural layers the matched nodes belong to.
  1. 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

More skills from Egonex-AI/Understand-Anything

  • AunderstandAnalyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships
  • Aunderstand-dashboardLaunch the interactive web dashboard to visualize a codebase's knowledge graph
  • Aunderstand-diffUse when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks
  • Aunderstand-domainExtract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (lightweight scan) or derives from an existing /understand knowledge graph.
  • Aunderstand-explainUse when you need a deep-dive explanation of a specific file, function, or module in the codebase
  • Aunderstand-figmaAnalyze a Figma file via the Figma REST API and generate an interactive design knowledge graph (pages, screens, components, component sets, instances, design tokens) with a kind:"design" dashboard.
  • Aunderstand-knowledgeAnalyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
  • Aunderstand-onboardUse when you need to generate an onboarding guide for new team members joining a project

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