# 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",

- Skill: `npc-worldwide/knowledge-store-skill` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add npc-worldwide/knowledge-store-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/npc-worldwide/knowledge-store-skill/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: npc-worldwide (https://skillmd.com/u/npc-worldwide)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/npc-worldwide/knowledge-store-skill

---


# 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

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

## 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"}
```