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Understand Explain

OrganizationPopular
Egonex-AI
understand-explain

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

Overview

PublisherEgonex-AI
RepositoryUnderstand-Anything
Skill nameunderstand-explain
Stars
83.2K
Forks
7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Egonex-AI on GitHub. Read the source before you install it.

Installation

Install the Understand Explain AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Egonex-AI/Understand-Anything.git /tmp/Understand-Anything
mkdir -p .claude/skills
cp -r /tmp/Understand-Anything/understand-anything-plugin/skills/understand-explain .claude/skills/understand-explain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Understand Explain in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Understand Explain on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Understand Explain is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/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 HEAD
      git diff --name-only "$GRAPH_COMMIT" HEAD -- .
      git diff --cached --name-only -- .
      git diff --name-only -- .
      git ls-files --others --exclude-standard -- .
    • The -- . pathspec is required: commits that only touch a sibling monorepo project must not make this graph stale. A hash mismatch alone is not stale when the project diff is empty.
    • Ignore the selected data directory (.ua/ or legacy .understand-anything/) in every command's output because it contains generated graph artifacts, not project source drift.
    • If the committed diff or any working-tree command reports project files, warn before explaining that graph-derived context may omit those changes. Suggest: Run /understand to refresh the graph.
    • Run the commit diff only when GRAPH_COMMIT_RAW resolves successfully. If the graph commit or Git metadata is missing, invalid, or unavailable, give a brief best-effort warning and continue instead of blocking.
  3. Find the target node — use Grep to search the knowledge graph for the component: "$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

Frequently asked questions

What does the Understand Explain AI skill do?

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

Why use Understand Explain on TypingMind?

Because you install it once and use it with any model. Understand Explain is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Understand Explain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Egonex-AI/Understand-Anything/tree/main/understand-anything-plugin/skills/understand-explain. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Understand Explain?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Understand Explain?

As many as you like. As long as a model supports skills, you can use Understand Explain with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Understand Explain AI skill free?

Yes. It is published on GitHub by Egonex-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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