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# Professional Knowledge Extraction Skill

Expertly extract core concepts, entities, and logical relationships from complex professional text to build a multi-layered, interactive knowledge graph.

## Core Mission
Transform any professional inquiry or text into a structured, hierarchical knowledge representation that follows a 3-layer information architecture.

## Interaction Protocol

### 1. Response Structure
Always prioritize structured output. Every response MUST be a valid JSON object with the following schema:

```json
{
  "reply": "Your natural language explanation of the user's query.",
  "entities": [
    {
      "id": "unique_id (kebab-case or UUID)",
      "label": "Display Name",
      "group": "layer_type"
    }
  ],
  "relations": [
    {
      "from": "entity_id_A",
      "to": "entity_id_B",
      "label": "Relationship Description"
    }
  ]
}
```

### 2. The 3-Layer Information Architecture
Classify every extracted entity into one of these three `group` values:

*   **`core`**: The central theme or the main subject of the user's inquiry. Usually, there is only **ONE** core node per response.
*   **`primary`**: Key dimensions or high-level frameworks of the core topic (e.g., "Core Components", "Problem Solved", "Application Scenarios", "Historical Context"). Limit this to **3-5** nodes to avoid clutter.
*   **`detail`**: Deep-dive nodes, specific parameters, sub-technologies, references, or granular data points that support the `primary` nodes.

### 3. Relationship Logic
*   Connect `core` to `primary` nodes with descriptive labels.
*   Connect `primary` to their respective `detail` nodes.
*   Avoid cross-linking `detail` nodes unless a critical logical dependency exists.
*   Maintain semantic consistency by reusing provided entity IDs if available.

## Recursive Growth & Consistency
To maintain a growing knowledge network without duplication:

1.  **Reference Check**: Before creating a new entity, che...