Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Clarified and simplified the "Environment Variables" section for improved readability. - Reformatted the env variable table and usage instructions. - No functional or code changes; documentation update only.
What This Skill Does
Adds semantic search to Aister's memory using PostgreSQL with pgvector and the e5-large-v2 embedding model. Queries match by meaning rather than exact keywords. Supports both English and Russian.
Finds relevant memory entries even when the query shares no words with the stored text, unlike grep or keyword search.
When to use it
- Finding past notes when you remember the topic but not the exact wording
- Searching stored preferences and settings by description
- Recalling what was written about a project without knowing precise terms
- Looking up identity or behavior notes by concept
- Retrieving context from memory files before starting a new conversation
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: /search_memory my communication style
- 1Converts the query to a 1024-dimension vector using e5-large-v2
- 2Runs cosine similarity search against stored vectors in PostgreSQL
- 3Filters results above the 0.5 similarity threshold
- 4Returns up to 5 matching memory chunks with source context
Excerpts from MEMORY.md or USER.md describing the user's communication preferences, ranked by semantic similarity
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.