knowledge_store_skill

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/npc-worldwide-knowledge-store-skill

About

Use cases: backend, ml, writing, agent-building, agent-memory, knowledge-base

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Runs as

Python package

What's inside

Skill 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.

License

MIT

Instructions

28 lines in SKILL.md · view on GitHub

# 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"}
```

Signals

Stars

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Forks

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6

Last commit (this folder)

Jun 20, 2026 (4 months ago)

Last release

Oct 12, 2026 (15 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License

MIT

Repository

github.com/NPC-Worldwide/npcpy

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

GitHubgithub-graphql