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Understand Domain

OrganizationPopular
Egonex-AI
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.

Overview

PublisherEgonex-AI
RepositoryUnderstand-Anything
Skill nameunderstand-domain
Stars
83.2K
Forks
7K
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Understand Domain 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-domain .claude/skills/understand-domain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Understand Domain 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 Domain 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 Domain 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-domain

Extracts business domain knowledge — domains, business flows, and process steps — from a codebase and produces an interactive horizontal flow graph in the dashboard.

How It Works

  • If a knowledge graph already exists (.ua/knowledge-graph.json, or the legacy .understand-anything/knowledge-graph.json when that directory is present), derives domain knowledge from it (cheap, no file scanning)
  • If no knowledge graph exists, performs a lightweight scan: file tree + entry point detection + sampled files
  • Use --full flag to force a fresh scan even if a knowledge graph exists

Instructions

Phase 0: Resolve PROJECT_ROOT

Set PROJECT_ROOT to the current working directory.

Worktree redirect. If PROJECT_ROOT is inside a git worktree (not the main checkout), redirect output to the main repository root. Worktrees managed by Claude Code are ephemeral — the data directory (.ua/, or legacy .understand-anything/) written there is destroyed when the session ends, taking the domain graph with it (issue #133). Detect a worktree by comparing git rev-parse --git-dir against git rev-parse --git-common-dir; in a normal checkout or submodule they resolve to the same path, in a worktree they differ and the parent of --git-common-dir is the main repo root.

bash
COMMON_DIR=$(git -C "$PROJECT_ROOT" rev-parse --git-common-dir 2>/dev/null)
GIT_DIR=$(git -C "$PROJECT_ROOT" rev-parse --git-dir 2>/dev/null)
if [ -n "$COMMON_DIR" ] && [ -n "$GIT_DIR" ]; then
  COMMON_ABS=$(cd "$PROJECT_ROOT" && cd "$COMMON_DIR" 2>/dev/null && pwd -P)
  GIT_ABS=$(cd "$PROJECT_ROOT" && cd "$GIT_DIR" 2>/dev/null && pwd -P)
  if [ -n "$COMMON_ABS" ] && [ "$COMMON_ABS" != "$GIT_ABS" ]; then
    MAIN_ROOT=$(dirname "$COMMON_ABS")
    if [ -d "$MAIN_ROOT" ] && [ "${UNDERSTAND_NO_WORKTREE_REDIRECT:-0}" != "1" ]; then
      echo "[understand-domain] Detected git worktree at $PROJECT_ROOT"
      echo "[understand-domain] Redirecting output to main repo root: $MAIN_ROOT"
      echo "[understand-domain] (Set UNDERSTAND_NO_WORKTREE_REDIRECT=1 to keep PROJECT_ROOT as the worktree.)"
      PROJECT_ROOT="$MAIN_ROOT"
    fi
  fi
fi

Use $PROJECT_ROOT (not the bare CWD) for every reference to "the current project" / <project-root> in subsequent phases.

Resolve the data directory $UA_DIR. All Understand-Anything artifacts live in the project's data directory. Resolve it once, now that $PROJECT_ROOT is known, and reuse $UA_DIR for every read and write in later phases:

bash
UA_DIR="$PROJECT_ROOT/$([ -d "$PROJECT_ROOT/.understand-anything" ] && echo .understand-anything || echo .ua)"

This keeps the legacy .understand-anything/ directory when it already exists (existing projects keep working with no migration) and uses the new .ua/ otherwise. Because each phase may run in a fresh shell, carry $UA_DIR forward like $PROJECT_ROOT, re-resolving it with the line above if a later command block needs it.

Important: do not assume the plugin root is simply two directories above the skill path string. In many installations ~/.agents/skills/understand-domain is a symlink into the real plugin checkout. Prefer runtime-provided plugin roots first (for Claude), then fall back to universal symlinks, skill symlink resolution, and common clone-based install paths.

Resolve the plugin root like this:

bash
SKILL_REAL=$(realpath ~/.agents/skills/understand-domain 2>/dev/null || readlink -f ~/.agents/skills/understand-domain 2>/dev/null || echo "")
SELF_RELATIVE=$([ -n "$SKILL_REAL" ] && cd "$SKILL_REAL/../.." 2>/dev/null && pwd || echo "")
COPILOT_SKILL_REAL=$(realpath ~/.copilot/skills/understand-domain 2>/dev/null || readlink -f ~/.copilot/skills/understand-domain 2>/dev/null || echo "")
COPILOT_SELF_RELATIVE=$([ -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 "$candidate" ] && [ -f "$candidate/package.json" ] && [ -f "$candidate/pnpm-workspace.yaml" ]; then
    PLUGIN_ROOT="$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-domain>}"
  echo "  - ${COPILOT_SELF_RELATIVE:-<unresolved path derived from ~/.copilot/skills/understand-domain>}"
  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 the plugin is installed correctly."
  exit 1
fi

Use $PLUGIN_ROOT for every reference to agent definitions in subsequent phases.

Phase 1: Detect Existing Graph

  1. Check if $UA_DIR/knowledge-graph.json exists
  2. If it exists AND --full was NOT passed, check freshness before deriving from it:
    • Read project.gitCommitHash from the graph metadata as GRAPH_COMMIT_RAW. Change to $PROJECT_ROOT, resolve it as a commit before using it in any Git diff, compare the resolved commit with git rev-parse HEAD, and inspect project-scoped committed and working-tree changes:
      bash
      GRAPH_COMMIT=$(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 that domain extraction may omit those changes. Suggest: Run /understand to refresh the knowledge 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. After that preflight, proceed to Phase 3 (derive from graph).
  4. Otherwise, proceed to Phase 2 (lightweight scan). When --full is used, skip this preflight because the command performs a fresh scan instead of consuming the existing graph.

Phase 2: Lightweight Scan (Path 1)

The preprocessing script does NOT produce a domain graph — it produces raw material (file tree, entry points, exports/imports) so the domain-analyzer agent can focus on the actual domain analysis instead of spending dozens of tool calls exploring the codebase. Think of it as a cheat sheet: cheap Python preprocessing → expensive LLM gets a clean, small input → better results for less cost.

  1. Run the preprocessing script bundled with this skill, passing $PROJECT_ROOT from Phase 0:
    python ./extract-domain-context.py "$PROJECT_ROOT"
    This outputs $UA_DIR/intermediate/domain-context.json containing:
    • File tree (respecting .gitignore)
    • Detected entry points (HTTP routes, CLI commands, event handlers, cron jobs, exported handlers)
    • File signatures (exports, imports per file)
    • Code snippets for each entry point (signature + first few lines)
    • Project metadata (package.json, README, etc.)
  2. Read the generated domain-context.json as context for Phase 4
  3. Proceed to Phase 4

Phase 3: Derive from Existing Graph (Path 2)

  1. Read $UA_DIR/knowledge-graph.json
  2. Format the graph data as structured context:
    • All nodes with their types, names, summaries, and tags
    • All edges with their types (especially calls, imports, contains)
    • All layers with their descriptions
    • Tour steps if available
  3. This is the context for the domain analyzer — no file reading needed
  4. Proceed to Phase 4

Phase 4: Domain Analysis

  1. Read the domain-analyzer agent prompt from $PLUGIN_ROOT/agents/domain-analyzer.md
  2. Dispatch a subagent with the domain-analyzer prompt + the context from Phase 2 or 3
  3. The agent writes its output to $UA_DIR/intermediate/domain-analysis.json

Phase 5: Validate and Save

  1. Read the domain analysis output
  2. Validate using the standard graph validation pipeline (the schema now supports domain/flow/step types)
  3. If validation fails, log warnings but save what's valid (error tolerance)
  4. Save to $UA_DIR/domain-graph.json
  5. Clean up $UA_DIR/intermediate/domain-analysis.json and $UA_DIR/intermediate/domain-context.json

Phase 6: Launch Dashboard

  1. Auto-trigger /understand-dashboard to visualize the domain graph
  2. The dashboard will detect domain-graph.json and show the domain view by default

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Understand Domain AI skill do?

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.

Why use Understand Domain on TypingMind?

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

Which AI models can use Understand Domain?

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 Domain?

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

Is the Understand Domain 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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