knowledge_store_skill
"Skill for working with local .knowledge.yaml files via KnowledgeStore.\
pinned to #631aad0updated 2 weeks ago
Ask your AI client: “install skills/knowledge-store-skill”.
Requires the metahub MCP server installed in your client. Set up MCP.
mh install skills/knowledge-store-skillmetahub onboarded this repo on the author's behalf.
If you own github.com/NPC-Worldwide/npcpy on GitHub, claim the listing to take over publishing. Your claim preserves the existing eval history and badges; only the curator label is replaced with verified-publisher on your next publish.
Stars
1,424
Last commit
2 weeks ago
Latest release
published
- #agents
- #ai
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- #mcp
- #mcp-client
- #mcp-server
- #ollama
- #perplexity
- #python
- #sql
- #yaml
About this skill
Pulled from SKILL.md at publish time.
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="pendi…
Evaluation report
WarningsAutomated checks the publisher passed at publish time — structure, docs, safety, and whether the artifact behaves as claimed.631aad0· 2 weeks ago
Documentation
41Description qualitywarn
9 words · 72 chars — skills use the description as their trigger; aim higher
Aim for 15+ words and include trigger phrases like “use this skill when …”.
README is present and substantial
38,856 chars · 16 sections · 47 code blocks
Tags / topics declared
11 total — agents, ai, llm, mcp, mcp-client, mcp-server (+5)
README has usage / example sections
found: Example · Example · Installation
Homepage / docs URL declared
no homepage declared (registry will use the repo URL) — info-only, not blocking
Release history
1- releasecurrent631aad0warn2 weeks ago
Contents
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
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"}
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mh install skills/knowledge-store-skill