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Understand Domain

  • 1.1k installs
  • 77.5k repo stars
  • Updated July 30, 2026
  • lum1104/understand-anything

understand-domain is an agent skill that runs extract-domain-context.py to scan a project directory and emit structured domain-context.json—sampling up to 5000 files—for developers onboarding LLM agents to unfamiliar cod

About

understand-domain is a skill from lum1104/understand-anything that scans any codebase and produces clean, structured domain context for LLM agents. The bundled extract-domain-context.py script walks a project root with limits of depth 6, 50 files per directory, 5000 files total, 40 sampled files, and capped lines per file, writing output to .understand-anything/intermediate/domain-context.json. A domain-analyzer agent then identifies business domains, flows, and steps. Developers reach for understand-domain when agents need accurate implementation or analysis in unfamiliar repositories without manual architecture tours.

  • Scans project directories while respecting MAX_FILES_TOTAL=5000 and MAX_SAMPLED_FILES=40 limits
  • Extracts structured JSON context focused on business domains, flows, and steps
  • Supports 20+ languages via SOURCE_EXTENSIONS including TypeScript, Python, Go, Rust, Java, and more
  • Outputs to .understand-anything/intermediate/domain-context.json for downstream domain-analyzer agents
  • Enforces MAX_OUTPUT_BYTES=512KB to stay within agent context windows

Understand Domain by the numbers

  • 1,125 all-time installs (skills.sh)
  • +24 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #965 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/lum1104/understand-anything --skill understand-domain

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Listed on Skillselion
Installs1.1k
repo stars77.5k
Security audit3 / 3 scanners passed
Last updatedJuly 30, 2026
Repositorylum1104/understand-anything

How do you extract domain context from a codebase?

Scan any codebase and generate clean, structured domain context that an LLM agent can immediately use for accurate analysis and implementation.

Who is it for?

Developers onboarding AI agents to large or unfamiliar repositories who need structured domain context before implementation tasks.

Skip if: Tiny single-file scripts or teams that already maintain complete architecture docs requiring no automated extraction.

When should I use this skill?

User asks to understand codebase domain, extract business context, or generate domain-context.json for agent analysis.

What you get

domain-context.json under .understand-anything/intermediate/ with scanned file tree, sampled sources, and domain flow metadata.

  • domain-context.json
  • Domain flow and step identification input

By the numbers

  • Scans up to 5000 files with max tree depth 6 and 50 files per directory
  • Samples up to 40 files into domain-context.json output

Files

SKILL.mdMarkdownGitHub ↗

/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 (.understand-anything/knowledge-graph.json), 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 — .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.

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.

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:

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 $PROJECT_ROOT/.understand-anything/knowledge-graph.json exists 2. If it exists AND --full was NOT passed → proceed to Phase 3 (derive from graph) 3. Otherwise → proceed to Phase 2 (lightweight scan)

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 $PROJECT_ROOT/.understand-anything/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 $PROJECT_ROOT/.understand-anything/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 $PROJECT_ROOT/.understand-anything/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 $PROJECT_ROOT/.understand-anything/domain-graph.json 5. Clean up $PROJECT_ROOT/.understand-anything/intermediate/domain-analysis.json and $PROJECT_ROOT/.understand-anything/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

Related skills

How it compares

Use understand-domain for automated domain JSON extraction; use architecture-doc skills when writing human-facing C4 or ADR documents.

FAQ

What file does understand-domain generate?

understand-domain runs extract-domain-context.py to write .understand-anything/intermediate/domain-context.json—a structured JSON context file the domain-analyzer agent uses for business domains, flows, and steps.

What scan limits does extract-domain-context.py enforce?

extract-domain-context.py caps file tree depth at 6, 50 files per directory, 5000 files total, and 40 sampled files—with per-file line limits—to keep domain extraction lightweight.

Is Understand Domain safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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