knowledgestoreskill
SkillFiles & storageLets your agent save, search, and update project memories stored in a local YAML file.
Then ask your AI: use the knowledgestoreskill skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.
About this skill
"Skill for working with local .knowledge.yaml files via KnowledgeStore.\
What this skill tells your AI
The instructions your AI receives, as published by npc-worldwide/npcpy in skills/knowledge_store_skill/SKILL.md and read by Ahel’s review.
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"}
Signals
- GitHub stars
- 2k
- Forks
- 124
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
knowledge-store-skill- Source
- github.com/npc-worldwide/npcpy
github.com/npc-worldwide/npcpy