understand-explain✓ تایید شده

Use when you need a deep-dive explanation of a specific file, function, or module in the codebase

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// نصب مهارت

نصب مهارت

مهارت‌ها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناخته‌شده را اسکن می‌کند اما نمی‌تواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.

نصب سراسری (سطح کاربر):

npx skillhub install Egonex-AI/Understand-Anything/understand-explain

نصب در پروژه فعلی:

npx skillhub install Egonex-AI/Understand-Anything/understand-explain --project

برای Hermes یا runtimeهای دیگر استفاده کنید. مهارت در مسیر <directory>/<skill-name>/ نوشته می‌شود.

npx skillhub install Egonex-AI/Understand-Anything/understand-explain --target-dir /path/to/skills

مسیر پیشنهادی: ~/.claude/skills/understand-explain/

بررسی هوش مصنوعی

64
از ۱۰۰
کیفیت دستورالعمل70
دقت توضیحات65
کاربردی بودن62
صحت فنی55

Scored 64 due to clear multi-step workflow for navigating knowledge graphs. Good structure but depends on the Understand-Anything ecosystem. Minor technical deduction for bash-only path resolution without cross-platform alternatives.

betamoderatedeveloperscode-reviewersonboarding-engineerscode-explanationarchitecture-analysisdependency-mapping
بررسی‌شده توسط review-skill-gateway(z-ai/glm-5.2) در تاریخ ۱۴۰۵/۴/۲۸

⚠ بررسی بر اساس نسخه قبلی

محتوای SKILL.md

---
name: understand-explain
description: Use when you need a deep-dive explanation of a specific file, function, or module in the codebase
argument-hint: "[file-path]"
---

# /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?}
  - 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

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. **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.

2. **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:
     ```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 before explaining that graph-derived context 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.

3. **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`

4. **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

5. **Read connected nodes** — for each connected node ID from step 4, Grep for those IDs in the nodes section to get their `name`, `summary`, and `type`. This builds the component's neighborhood.

6. **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.

7. **Read the actual source file** — Read the source file at the node's `filePath` for the deep-dive analysis.

8. **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 understanding

مجوز

مجوز اعلام‌شده: MIT

MIT License

Copyright (c) 2026 Egonex-AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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