| name | knowledge_store_skill |
| description | Skill for working with local .knowledge.yaml files via KnowledgeStore. Use this when you need to recall, search, or manage directory-local memories and knowledge links stored in plain YAML alongside the user's project files.
KnowledgeStore is directory-scoped. Each directory that contains a `.knowledge.yaml` file maintains its own append-only memory graph. There is no global database — the YAML IS the source of truth.
Key operations: - Load a directory's store: `npcpy.memory.knowledge_store.get_store_for_path(path)` - Append a memory: `store.append_memory(initial_memory="...", status="pending_approval", ...)` - Update a memory (approve/reject/edit): `store.update_memory(mem_id, status, final_memory)` - Search memories (keyword substring): `store.search_memories("query", limit=20)` - Get approved context for LLM prompts: `store.build_context(max_memories=10)` - Get links for a memory: `store.get_links_for_memory(mem_id)` - Create a link between memories: `store.append_link(from_mem, to_mem, relation="refines", agent="your_name")` - Aggregate across a tree: `KnowledgeStore.aggregate(root_directory, max_depth=3)`
Memory statuses: - `pending_approval` — raw extraction, needs human review - `human-approved` — confirmed and available for context injection - `human-rejected` — discard, can be used as negative examples - `human-edited` — corrected version supersedes initial_memory
When answering questions, prefer `build_context()` for recently approved local knowledge, and `search_memories()` for targeted recall. Always respect `human-rejected` memories — do not repeat them. |
knowledge_store_skill
Skill for working with local .knowledge.yaml files via KnowledgeStore. Use this when you need to recall, search, or manage directory-local memories and knowledge links stored in plain YAML alongside the user's project files.
KnowledgeStore is directory-scoped. Each directory that contains a .knowledge.yaml file maintains its own append-only memory graph. There is no global database — the YAML IS the source of truth.
Key operations: - Load a directory's store: npcpy.memory.knowledge_store.get_store_for_path(path) - Append a memory: store.append_memory(initial_memory="...", status="pending_approval", ...) - Update a memory (approve/reject/edit): store.update_memory(mem_id, status, final_memory) - Search memories (keyword substring): store.search_memories("query", limit=20) - Get approved context for LLM prompts: store.build_context(max_memories=10) - Get links for a memory: store.get_links_for_memory(mem_id) - Create a link between memories: store.append_link(from_mem, to_mem, relation="refines", agent="your_name") - Aggregate across a tree: KnowledgeStore.aggregate(root_directory, max_depth=3)
Memory statuses: - pending_approval — raw extraction, needs human review - human-approved — confirmed and available for context injection - human-rejected — discard, can be used as negative examples - human-edited — corrected version supersedes initial_memory
When answering questions, prefer build_context() for recently approved local knowledge, and search_memories() for targeted recall. Always respect human-rejected memories — do not repeat them.
Inputs
name (default: 'action')description (default: 'load | search | append | update | link | context | aggregate')name (default: 'directory_path')description (default: 'Absolute path of the directory containing .knowledge.yaml')name (default: 'query_or_memory')description (default: 'Search query, memory text, or JSON params depending on action')
Steps
Usage
/run_jinx jinx_ref=knowledge_store_skill input_values={"name": "query_or_memory", "description": "Search query, memory text, or JSON params depending on action"}
