mcpbeat

Understand Onboard

egonex-ai/understand-onboard

Use when you need to generate an onboarding guide for new team members joining a project

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
77339
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/Egonex-AI/Understand-Anything --skill understand-onboard

The instruction itself

4 sections, as written by the author

/understand-onboard

Generate a comprehensive onboarding guide from the project's knowledge graph.

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

  • Use Grep to search within the JSON for relevant entries BEFORE reading the full file
  • Only read sections you need — don't dump the entire graph into context
  • Node names and summaries are the most useful fields for understanding
  • Edges tell you how components connect — follow imports and calls for dependency chains

Instructions

  • 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 $UA_DIR/knowledge-graph.json exists. If not, tell the user to run /understand first.
  • Check graph freshness before using graph-derived context:
  • Read project.gitCommitHash from the graph metadata as GRAPH_COMMIT_RAW. Resolve it as a commit before using it in any Git diff, then compare it with git rev-parse HEAD and inspect project-scoped committed and working-tree changes from the project root:
     GRAPH_COMMIT=$(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 generating the guide that onboarding content 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.
  • Read project metadata — use Grep or Read with a line limit to extract the "project" section (name, description, languages, frameworks).
  • Read layers — Grep for "layers" to get the full layers array. These define the architecture and will structure the guide.
  • Read the tour — Grep for "tour" to get the guided walkthrough steps. These provide the recommended learning path.
  • Read file-level structural nodes only — use Grep to find nodes with file-level types (file, config, document, service, pipeline, table, schema, resource, endpoint) in the knowledge graph. Skip function-level and class-level nodes to keep the guide high-level. Extract each node's name, filePath, summary, and complexity.
  • Identify complexity hotspots — from the file-level nodes, find those with the highest complexity values. These are areas new developers should approach carefully.
  • Generate the onboarding guide with these sections:
  • Project Overview: name, languages, frameworks, description (from project metadata)
  • Architecture Layers: each layer's name, description, and key files (from layers + file nodes)
  • Key Concepts: important patterns and design decisions (from node summaries and tags)
  • Guided Tour: step-by-step walkthrough (from the tour section)
  • File Map: what each key file does (from file-level nodes, organized by layer)
  • Complexity Hotspots: areas to approach carefully (from complexity values)
  • Format as clean markdown

10. Offer to save the guide to docs/ONBOARDING.md in the project

11. Suggest the user commit it to the repo for the team

How to use it

Copy the folder

Take egonex-ai/understand-onboard from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.