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.
Restart Claude Code after copying so it picks up the new skill.
Use it in TypingMind
Enable Knowledge Graph Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.
SkillsLoad skill"knowledge_graph_skill"
Gemini 3.5 Pro
The model loads Knowledge Graph Skill on its own as soon as a request matches it.
Works with any AI model
AI skills are plain Markdown instructions rather than provider-specific code, so Knowledge Graph Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.
Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.
Skill instructions
This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.
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):
Keyword search — fast substring match over fact statements.
kg_search_facts(engine_or_kg, "keyword") → List[str]
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'
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)')
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 embeddi...
Why use Knowledge Graph Skill on TypingMind?
Because you install it once and use it with any model. Knowledge Graph Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.
How do I install Knowledge Graph Skill in TypingMind?
Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NPC-Worldwide/npcpy/tree/main/skills/knowledge_graph_skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.
Which AI models can use Knowledge Graph Skill?
Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.
How many AI models can I use with Knowledge Graph Skill?
As many as you like. As long as a model supports skills, you can use Knowledge Graph Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.
Is the Knowledge Graph Skill AI skill free?
Yes. It is published on GitHub by NPC-Worldwide under the MIT license. You only pay your own AI provider for the tokens you use.
What are AI skills?
An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.
How are AI skills different from plugins or MCP servers?
A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.
