TurboQuant Context

Compress conversation context into a TurboQuant vector store inside OpenClaw memory, then retrieve the most relevant entries on demand to stay within token b...

This skill is coherent with its stated purpose, but check a few practical things before installing: (1) ensure the 'uv' CLI you provide is a trusted implementation (inspect the upstream repo https://github.com/openclaw/openclaw-turboquant if you want to review code), (2) be aware the skill will create and read files under ~/.openclaw/memory/, so avoid placing highly sensitive data there unless you accept local persistence, (3) embeddings must be produced by you and saved as .npy files the tool will read—confirm your workflow for generating and securing those files, and (4) if you need stricter privacy, consider encrypting the memory directory or using ephemeral session IDs so the store does not persist across restarts.

Type: OpenClaw Skill Name: turboquant Version: 0.1.0 The turboquant skill is a utility for managing conversation context through vector embeddings and local storage. It provides standard commands (ingest, assemble, compact) for RAG-style context management and uses the 'uv' package manager to execute its operations. All data storage is confined to the local directory (~/.openclaw/memory/), and the instructions in SKILL.md are purely functional without any signs of malicious prompt injection, data exfiltration, or unauthorized execution.