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Understand Dashboard

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Egonex-AI
understand-dashboard

Launch the interactive web dashboard to visualize a codebase's knowledge graph

Overview

PublisherEgonex-AI
RepositoryUnderstand-Anything
Skill nameunderstand-dashboard
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 Dashboard 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-dashboard .claude/skills/understand-dashboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Understand Dashboard 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 Dashboard 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 Dashboard 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-dashboard

Start the Understand Anything dashboard to visualize the knowledge graph for the current project.

Instructions

  1. Determine the project directory and data directory:

    • If $ARGUMENTS contains a path, use that as the project directory
    • Otherwise, use the current working directory
    • Prefer the legacy .understand-anything/ data directory when it exists, otherwise use .ua/

    Use the Bash tool to resolve:

    bash
    PROJECT_ARG="$ARGUMENTS"
    if [ -n "$PROJECT_ARG" ]; then
      PROJECT_DIR=$(cd "$PROJECT_ARG" 2>/dev/null && pwd -P)
    else
      PROJECT_DIR=$(pwd -P)
    fi
    
    if [ -z "$PROJECT_DIR" ] || [ ! -d "$PROJECT_DIR" ]; then
      echo "Error: Project directory not found: ${PROJECT_ARG:-$PWD}"
      exit 1
    fi
    
    if [ -d "$PROJECT_DIR/.understand-anything" ]; then
      UA_DIR="$PROJECT_DIR/.understand-anything"
    else
      UA_DIR="$PROJECT_DIR/.ua"
    fi
  2. Check that $UA_DIR/knowledge-graph.json exists in the project directory. If not, tell the user:

    No knowledge graph found. Run /understand first to analyze this project.

    Use the Bash tool to check:

    bash
    if [ ! -f "$UA_DIR/knowledge-graph.json" ]; then
      echo "No knowledge graph found. Run /understand first to analyze this project."
      exit 1
    fi
  3. Find the dashboard code. The dashboard is at packages/dashboard/ relative to this plugin's root directory. Check these paths in order and use the first that exists:

    • ${CLAUDE_PLUGIN_ROOT}/packages/dashboard/ (Claude Code runtime root, highest priority)
    • ~/.understand-anything-plugin/packages/dashboard/ (universal symlink, all installs)
    • Two levels up from ~/.agents/skills/understand-dashboard real path (self-relative fallback)
    • Two levels up from ~/.copilot/skills/understand-dashboard real path (Copilot personal skills fallback)
    • Common clone-based install roots:
      • ~/.codex/understand-anything/understand-anything-plugin/packages/dashboard/
      • ~/.opencode/understand-anything/understand-anything-plugin/packages/dashboard/
      • ~/.pi/understand-anything/understand-anything-plugin/packages/dashboard/
      • ~/understand-anything/understand-anything-plugin/packages/dashboard/

    Use the Bash tool to resolve:

    bash
    SKILL_REAL=$(realpath ~/.agents/skills/understand-dashboard 2>/dev/null || readlink -f ~/.agents/skills/understand-dashboard 2>/dev/null || echo "")
    SELF_RELATIVE=$([ -n "$SKILL_REAL" ] && cd "$SKILL_REAL/../.." 2>/dev/null && pwd || echo "")
    COPILOT_SKILL_REAL=$(realpath ~/.copilot/skills/understand-dashboard 2>/dev/null || readlink -f ~/.copilot/skills/understand-dashboard 2>/dev/null || echo "")
    COPILOT_SELF_RELATIVE=$([ -n "$COPILOT_SKILL_REAL" ] && cd "$COPILOT_SKILL_REAL/../.." 2>/dev/null && pwd || echo "")
    
    PLUGIN_ROOT=""
    for candidate in \
      "${CLAUDE_PLUGIN_ROOT}" \
      "$HOME/.understand-anything-plugin" \
      "$SELF_RELATIVE" \
      "$COPILOT_SELF_RELATIVE" \
      "$HOME/.codex/understand-anything/understand-anything-plugin" \
      "$HOME/.opencode/understand-anything/understand-anything-plugin" \
      "$HOME/.pi/understand-anything/understand-anything-plugin" \
      "$HOME/understand-anything/understand-anything-plugin"; do
      if [ -n "$candidate" ] && [ -d "$candidate/packages/dashboard" ]; then
        PLUGIN_ROOT="$candidate"; break
      fi
    done
    
    if [ -z "$PLUGIN_ROOT" ]; then
      echo "Error: Cannot find the understand-anything plugin root."
      echo "Checked:"
      echo "  - ${CLAUDE_PLUGIN_ROOT:-<unset CLAUDE_PLUGIN_ROOT>}"
      echo "  - $HOME/.understand-anything-plugin"
      echo "  - ${SELF_RELATIVE:-<unresolved path derived from ~/.agents/skills/understand-dashboard>}"
      echo "  - ${COPILOT_SELF_RELATIVE:-<unresolved path derived from ~/.copilot/skills/understand-dashboard>}"
      echo "  - $HOME/.codex/understand-anything/understand-anything-plugin"
      echo "  - $HOME/.opencode/understand-anything/understand-anything-plugin"
      echo "  - $HOME/.pi/understand-anything/understand-anything-plugin"
      echo "  - $HOME/understand-anything/understand-anything-plugin"
      echo "Make sure you followed the installation instructions for your platform."
      exit 1
    fi
    
    DASHBOARD_DIR="$PLUGIN_ROOT/packages/dashboard"
  4. Fast path — try the prebuilt viewer first (no install, no build). Each release ships a self-contained viewer tarball; run it pinned to the installed plugin version:

    bash
    : "${PLUGIN_ROOT:?Run step 3 first so PLUGIN_ROOT is set}"
    : "${PROJECT_DIR:?Run step 1 first so PROJECT_DIR is set}"
    PLUGIN_VERSION=$(node -p "require('$PLUGIN_ROOT/package.json').version")
    VIEWER_URL="https://github.com/Egonex-AI/Understand-Anything/releases/download/v${PLUGIN_VERSION}/understand-anything-viewer.tgz"
    npx --yes "$VIEWER_URL" "$PROJECT_DIR"

    Run this in the background. It prints the same 🔑 Dashboard URL line as the dev server:

    • If the line appears, skip steps 5-6 and continue at step 7.
    • If the process exits without printing it (no release asset for this version, or no network), fall back to steps 5-6.
  5. Fallback: install dependencies and build if needed:

    bash
    : "${PLUGIN_ROOT:?Run step 3 first so PLUGIN_ROOT is set}"
    DASHBOARD_DIR="${DASHBOARD_DIR:-$PLUGIN_ROOT/packages/dashboard}"
    cd "$DASHBOARD_DIR" && (pnpm install --frozen-lockfile 2>/dev/null || pnpm install)

    Then ensure the core package is built (the dashboard depends on it):

    bash
    : "${PLUGIN_ROOT:?Run step 3 first so PLUGIN_ROOT is set}"
    cd "$PLUGIN_ROOT" && pnpm --filter @understand-anything/core build
  6. Fallback: start the Vite dev server pointing at the project's knowledge graph:

    bash
    : "${PROJECT_DIR:?Run step 1 first so PROJECT_DIR is set}"
    : "${DASHBOARD_DIR:?Run step 5 first so DASHBOARD_DIR is set}"
    cd "$DASHBOARD_DIR" && GRAPH_DIR="$PROJECT_DIR" npx vite --host 127.0.0.1

    Run this in the background so the user can continue working.

  7. Capture the access token URL from the server output. The server (viewer or Vite) prints a line like:

    🔑  Dashboard URL: http://127.0.0.1:<PORT>?token=<TOKEN>

    Extract the full URL including the ?token= parameter. The token is required to access the knowledge graph data — without it the dashboard will show an "Access Token Required" gate.

  8. Report to the user, including the full tokenized URL:

    Dashboard started at http://127.0.0.1:<PORT>?token=<TOKEN>
    Viewing: $UA_DIR/knowledge-graph.json
    
    The dashboard is running in the background. Press Ctrl+C in the terminal to stop it.

    Important: Always include the ?token= parameter in the URL you share. If you omit it, the user will be blocked by the token gate and have to manually find the token in the terminal output.

Notes

  • The fast path (step 4) downloads a version-pinned, self-contained viewer from the GitHub release — nothing is installed into the plugin directory and no build runs
  • The dashboard auto-opens in the default browser (both the viewer and Vite's --open)
  • If port 5173 is already in use, the next available port is picked (both paths)
  • In the fallback, the GRAPH_DIR environment variable tells the dev server where to find the knowledge graph

Frequently asked questions

What does the Understand Dashboard AI skill do?

Launch the interactive web dashboard to visualize a codebase's knowledge graph

Why use Understand Dashboard on TypingMind?

Because you install it once and use it with any model. Understand Dashboard 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 Dashboard 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-dashboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Understand Dashboard?

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 Dashboard?

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

Is the Understand Dashboard 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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