Understand Explain

作者 egonex-ai790b15702863無授權條款85K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫2 天前更新

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

AI 產生的概覽

運用預先建立的知識圖譜及其原始碼,說明程式碼庫中某個檔案、函式或模組。

功能
此技能會針對單一程式碼元件(例如檔案、函式、類別或模組)產出深入說明。它會在知識圖譜 JSON 中定位該元件,蒐集其關聯邊、所屬分層與相鄰節點,再讀取實際原始碼檔案,說明其架構角色、內部結構、相依關係與資料流。它也會檢查圖譜相對於目前 Git 提交是否過期,並在可能遺漏專案變更時提出提醒。
適用情境
當需要深入解說程式碼庫中某個特定檔案、函式或模組時使用。適合新手熟悉環境、理解程式碼,以及了解某元件與專案其他部分的關聯。它依賴既有的知識圖譜,因此不適用於尚未分析過的專案。
執行需求
需要既有的知識圖譜,位於 .ua/knowledge-graph.json 或舊版 .understand-anything/knowledge-graph.json,由獨立的 /understand 步驟產生。它使用 Grep 與檔案讀取,並可選用 Git 指令檢查圖譜新舊;Git 中繼資料為盡力而為,缺少時不會阻擋解說。它不附帶任何指令碼。

/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 HEADgit 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 understanding

來源與署名

來源:egonex-ai/understand-anything位於understand-anything-plugin/skills/understand-explain提交790b157

授權條款: 無授權條款

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