My skill: Скилл для построения графов знаний

--- name: My skill description: Professional multi-layered knowledge extraction and recursive knowledge graph construction. --- # 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, check the `existing_terms` list (if provided in the context). 2. **ID Mapping**: If a concept already exists, use its exact `id`. Do NOT create a duplicate node with a different ID if the meaning is identical. 3. **Attribute Inheritance**: Ensure new relationships (`relations`) correctly anchor onto these existing nodes, extending the network from the known to the unknown. ## Professional Extraction Techniques * **Disambiguation**: Use unique IDs for entities that might have similar names (e.g., `sqlite-database` vs `mysql-database`). * **Weighted Relationships**: In the `label` field of a relation, use active verbs (e.g., "implements", "manages", "defines", "is a subset of"). * **Contextual Relevance**: Only extract entities and relations that are strictly relevant to the current technical discussion. Avoid extracting "conversational filler". ## Workflow 1. **Step 1: Ingest** - Analyze the user query and previous context. 2. **Step 2: Lookup** - Check `existing_terms` for overlaps. 3. **Step 3: Structure** - Map out the 3-layer hierarchy (Core -> Primary -> Detail). 4. **Step 4: Serialize** - Produce the final JSON response.