/knowledge_graph_skill

name: knowledge_graph_skill
description: "Skill for searching and evolving the SQLite-backed Knowledge Graph.\
  \ Use this when you need structured fact/concept/link search across one or more\
  \ teams, NPCs, or directory scopes.\nThe Knowledge Graph (KG) is stored in the application's\
  \ database (not YAML). It is scoped by (team_name, npc_name, directory_path). Facts\
  \ and concepts carry generation numbers and origin tags.\nSearch methods (choose\
  \ the right one):\n1. Keyword search \u2014 fast substring match over fact statements.\n\
  \   `kg_search_facts(engine_or_kg, \"keyword\")` \u2192 List[str]\n\n2. Embedding\
  \ search \u2014 semantic cosine similarity via vector embeddings.\n   `kg_embedding_search(engine_or_kg,\
  \ query=\"...\", embedding_model=\"nomic-embed-text\",\n    embedding_provider=\"\
  ollama\", similarity_threshold=0.6, max_results=20)`\n   \u2192 List[dict] with\
  \ 'content', 'type', 'score'\n\n3. Link search \u2014 graph traversal (BFS/DFS)\
  \ starting from keyword-matched seeds.\n   `kg_link_search(engine_or_kg, query=\"\
  ...\", max_depth=2, breadth_per_step=5,\n    strategy=\"bfs\", max_results=20)`\n\
  \   \u2192 List[dict] with 'content', 'type', 'depth', 'path', 'score'\n\n4. Hybrid\
  \ search \u2014 combines keyword + embedding + link, boosting results\n   found\
  \ by multiple methods.\n   `kg_hybrid_search(engine_or_kg, query=\"...\", mode=\"\
  all\", max_depth=2,\n    similarity_threshold=0.6, max_results=20)`\n   \u2192 List[dict]\
  \ with 'content', 'type', 'score', 'source'\n\nGraph evolution (use sparingly, usually\
  \ in background): - `kg_initial(content, model, provider)` \u2014 build a new KG\
  \ from text - `kg_evolve_incremental(existing_kg, new_content_text, ...)` \u2014\
  \ add content - `kg_sleep_process(existing_kg, model, provider)` \u2014 prune/deepen/consolidate\
  \ - `kg_dream_process(existing_kg, model, provider, num_seeds)` \u2014 speculative\
  \ synthesis\nWhen a user asks a question that spans facts, concepts, and their relationships,\
  \ prefer hybrid search. For pure semantic similarity without graph structure, use\
  \ embedding search. For exploring connected neighborhoods, use link search with\
  \ BFS."

knowledge_graph_skill

Skill for searching and evolving the SQLite-backed Knowledge Graph. Use this when you need structured fact/concept/link search across one or more teams, NPCs, or directory scopes. The Knowledge Graph (KG) is stored in the application's database (not YAML). It is scoped by (team_name, npc_name, directory_path). Facts and concepts carry generation numbers and origin tags. Search methods (choose the right one): 1. Keyword search — fast substring match over fact statements. `kg_search_facts(engine_or_kg, "keyword")` → List[str]

2. Embedding search — semantic cosine similarity via vector embeddings. `kg_embedding_search(engine_or_kg, query="...", embedding_model="nomic-embed-text", embedding_provider="ollama", similarity_threshold=0.6, max_results=20)` → List[dict] with 'content', 'type', 'score'

3. Link search — graph traversal (BFS/DFS) starting from keyword-matched seeds. `kg_link_search(engine_or_kg, query="...", max_depth=2, breadth_per_step=5, strategy="bfs", max_results=20)` → List[dict] with 'content', 'type', 'depth', 'path', 'score'

4. Hybrid search — combines keyword + embedding + link, boosting results found by multiple methods. `kg_hybrid_search(engine_or_kg, query="...", mode="all", max_depth=2, similarity_threshold=0.6, max_results=20)` → List[dict] with 'content', 'type', 'score', 'source'

Graph evolution (use sparingly, usually in background): - `kg_initial(content, model, provider)` — build a new KG from text - `kg_evolve_incremental(existing_kg, new_content_text, ...)` — add content - `kg_sleep_process(existing_kg, model, provider)` — prune/deepen/consolidate - `kg_dream_process(existing_kg, model, provider, num_seeds)` — speculative synthesis When a user asks a question that spans facts, concepts, and their relationships, prefer hybrid search. For pure semantic similarity without graph structure, use embedding search. For exploring connected neighborhoods, use link search with BFS.

Inputs

• `name` (default: `'search_method'`)
• `description` (default: `'keyword | embedding | link | hybrid'`)
• `name` (default: `'query'`)
• `description` (default: `"The user's query or topic to search"`)
• `name` (default: `'scope_team'`)
• `description` (default: `'Team name to scope the search (optional)'`)
• `name` (default: `'scope_npc'`)
• `description` (default: `'NPC name to scope the search (optional)'`)
• `name` (default: `'scope_directory'`)
• `description` (default: `'Directory path to scope the search (optional)'`)

Steps

• `instruct` → [`instruct.py`](./instruct.py)

Usage

/run_jinx jinx_ref=knowledge_graph_skill input_values={"name": "scope_directory", "description": "Directory path to scope the search (optional)"}