understand-chat 精选
Use when you need to ask questions about a codebase or understand code using a knowledge graph
安装方式
cp -r understand-anything-plugin/skills/understand-chat ~/.claude/skills/understand-chat把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/
/plugin marketplace add Egonex-AI/Understand-Anything⚠️ 该命令形态随客户端版本变化,仅在检测到 .claude-plugin/marketplace.json 时展示;不可用时请用「目录复制」。
https://raw.githubusercontent.com/Egonex-AI/Understand-Anything/f2d0164f72fe678805c34245a6df36acededfebd/understand-anything-plugin/skills/understand-chat/SKILL.md下载后另存为 ~/.claude/skills/understand-chat/SKILL.md。链接锁定采集时的 commit,内容与本站展示一致。
整仓一次性获取:git clone --depth 1 https://github.com/Egonex-AI/Understand-Anything.git
依赖与权限
- 无需 API Key
- 需要联网访问外部服务
- 依赖外部命令:
gitnodemake - 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。
SKILL.md 正文
/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
- 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$UA_DIR/knowledge-graph.jsonexists in the current project root. If not, tell the user to run/understandfirst.
- Check graph freshness before using graph-derived context:
- Read
project.gitCommitHashfrom the graph metadata asGRAPH_COMMIT_RAW. Resolve it as a commit before using it in any Git diff, then compare it withgit rev-parse HEADand inspect project-scoped committed and working-tree changes from the project root:
GRAPH_COMMIT=$(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 answering 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 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).
- 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
idvalues of all matching nodes
- Find connected edges — for each matched node ID, Grep for that ID in the
edgessection 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
- Read layer context — Grep for
"layers"to understand which architectural layers the matched nodes belong to.
- 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
质量与来源
- 所属仓库
- Egonex-AI/Understand-Anything
- 仓库星标
- 8.6 万
- 最近更新
- 今天
- 采集时间
- 今天
- commit
- f2d0164f72fe
- 内容指纹
- sha256:5f1da70e0849ec2
- 文件路径
- understand-anything-plugin/skills/understand-chat/SKILL.md
同仓库其它 Skill
查看全部 9 个understand-dashboard
Launch the interactive web dashboard to visualize a codebase's knowledge graph
understand-diff
Use when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks
understand-domain
Extract 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.
understand-explain
Use when you need a deep-dive explanation of a specific file, function, or module in the codebase
understand-figma
Analyze 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.
understand-knowledge
Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
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