Understand Chat

作者 egonex-ai790b15702863无许可证85K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库2天前更新

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

AI 生成的概览

通过检索预构建的知识图谱 JSON 文件来回答有关代码库的问题。

功能
该技能引导智能体使用项目数据目录中的知识图谱(.ua/knowledge-graph.json,或旧版 .understand-anything/knowledge-graph.json)回答代码库相关问题。它说明了图谱结构(项目元数据、节点、边、层、导览),并规定了工作流程:确定数据目录、对照 Git 提交检查图谱新鲜度、读取项目元数据、在节点名称/摘要/标签中检索查询关键词、沿边查找一跳子图、读取层上下文。产出是引用图谱中具体文件、函数、关系和架构层的简洁回答。
适用场景
当你需要就代码库提问,或通过已有知识图谱而非直接阅读源码来理解代码时使用。适用于对已完成分析的项目进行探索、上手和依赖关系提问。前提是图谱已存在,否则它会提示用户先运行 /understand。
运行要求
需要项目中已存在知识图谱 JSON(.ua/knowledge-graph.json 或旧版 .understand-anything/knowledge-graph.json)、可用于新鲜度检查的 Git,以及 Grep、Read 等文件搜索与读取工具。该技能不附带脚本,仅为指令。

/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, 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 in the current project root. 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 answering 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. 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).

  4. 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
  5. 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
  6. Read layer context — Grep for "layers" to understand which architectural layers the matched nodes belong to.

  7. 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

来源与署名

来源:egonex-ai/understand-anything位于understand-anything-plugin/skills/understand-chat提交790b157

许可证: 无许可证

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