/understand-explain
Provide a thorough, in-depth explanation of a specific code component.
Best use case
/understand-explain is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Provide a thorough, in-depth explanation of a specific code component.
Teams using /understand-explain should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/understand-explain/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How /understand-explain Compares
| Feature / Agent | /understand-explain | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Provide a thorough, in-depth explanation of a specific code component.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# /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?}
- Node types: file, function, class, module, concept
- IDs: `file:path`, `func:path:name`, `class:path:name`
- `edges[]` — each has {source, target, type, direction, weight}
- Key types: imports, contains, calls, depends_on
- `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. Check that `.understand-anything/knowledge-graph.json` exists. If not, tell the user to run `/understand` first.
2. **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`
3. **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
4. **Read connected nodes** — for each connected node ID from step 3, Grep for those IDs in the nodes section to get their `name`, `summary`, and `type`. This builds the component's neighborhood.
5. **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.
6. **Read the actual source file** — Read the source file at the node's `filePath` for the deep-dive analysis.
7. **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 understandingRelated Skills
model-explainability-tool
Model Explainability Tool - Auto-activating skill for ML Training. Triggers on: model explainability tool, model explainability tool Part of the ML Training skill category.
explaining-machine-learning-models
Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
code-documentation-code-explain
You are a code education expert specializing in explaining complex code through clear narratives, visual diagrams, and step-by-step breakdowns. Transform difficult concepts into understandable explanations.
azure-ai-contentunderstanding-py
Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".
explain-expert
Analyze codebases and provide technical overviews covering core components, interactions, deployment architecture, and runtime behavior. Use when the user asks for codebase analysis, project overview, technical architecture explanation, or system documentation.
code-explainer
Explain complex code to team members in clear, understandable terms for effective knowledge shari...
understanding-db-schema
Deep expertise in Logseq's Datascript database schema. Auto-invokes when users ask about Logseq DB schema, Datascript attributes, built-in classes, property types, entity relationships, schema validation, or the node/block/page data model. Provides authoritative knowledge of the DB graph architecture.
User Research — Understanding Users Through Evidence
## Overview
SKILL: Week 6: Understanding Windows Mitigations
## Metadata
/understand
Analyze the current codebase and produce a `knowledge-graph.json` file in `.understand-anything/`. This file powers the interactive dashboard for exploring the project's architecture.
/understand-onboard
Generate a comprehensive onboarding guide from the project's knowledge graph.
/understand-diff
Analyze the current code changes against the knowledge graph at `.understand-anything/knowledge-graph.json`.