/understand-chat
Answer questions about this codebase using the knowledge graph at `.understand-anything/knowledge-graph.json`.
Best use case
/understand-chat is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Answer questions about this codebase using the knowledge graph at `.understand-anything/knowledge-graph.json`.
Teams using /understand-chat should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/understand-chat/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How /understand-chat Compares
| Feature / Agent | /understand-chat | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Answer questions about this codebase using the knowledge graph at `.understand-anything/knowledge-graph.json`.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# /understand-chat
Answer questions about this codebase using the knowledge graph at `.understand-anything/knowledge-graph.json`.
## 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?}
- Node types: file, function, class, module, concept
- IDs: `file:path`, `func:path:name`, `class:path:name`
- `edges[]` — each has {source, target, type, direction, weight}
- Key types: imports, contains, calls, depends_on
- `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. Check that `.understand-anything/knowledge-graph.json` exists in the current project root. If not, tell the user to run `/understand` first.
2. **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).
3. **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
4. **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
5. **Read layer context** — Grep for `"layers"` to understand which architectural layers the matched nodes belong to.
6. **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 graphRelated Skills
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