Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
egonex-ai avatar

Understand Chat

  • 703 installs
  • 77.5k repo stars
  • Updated July 30, 2026
  • egonex-ai/understand-anything

This is a copy of understand-chat by lum1104 - installs and ranking accrue to the original listing.

Helps with ai & agent building tasks.

About

understand-chat is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • understand-chat
  • AI & Agent Building
  • AI-coding skill

Understand Chat by the numbers

  • 703 all-time installs (skills.sh)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/egonex-ai/understand-anything --skill understand-chat

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs703
repo stars77.5k
Last updatedJuly 30, 2026
Repositoryegonex-ai/understand-anything

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

/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?}
  • 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. 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 graph

Related skills

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.