TurboQuant Context
v0.1.0Compress conversation context into a TurboQuant vector store inside OpenClaw memory, then retrieve the most relevant entries on demand to stay within token b...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (compress conversation context into a compressed vector store) match the instructions: ingest, assemble, compact, and store-info commands all operate on a local store path. The declared dependency ('uv') is the only external requirement and matches the CLI invocations shown.
Instruction Scope
SKILL.md only instructs the agent to run the uv CLI against a local store and local .npy/.npz files, and to persist data under ~/.openclaw/memory/turboquant-$OPENCLAW_SESSION_ID. It does not ask the agent to read unrelated system files, call external web endpoints, or exfiltrate data. It does assume the caller produces embeddings and places them in files the tool will read.
Install Mechanism
There is no install spec (instruction-only), so nothing will be downloaded or written by the skill itself. The only runtime requirement is that a trusted 'uv' binary be available on PATH; the skill also mentions running 'uv sync' once — this is an operational note but not an installer.
Credentials
No environment variables, credentials, or config paths are required by the skill. It references $OPENCLAW_SESSION_ID and $TURN_NUMBER as context-derived names for store paths/ids, which is proportionate to maintaining per-session memory.
Persistence & Privilege
The skill persists a store under the user's home (~/.openclaw/memory/...), which is expected for a memory compression tool. always is false and autonomous invocation is allowed (platform default). Persistence across restarts is a design choice appropriate for the stated purpose but users should be aware data will be stored on disk.
Assessment
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.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
⚡ Clawdis
Binsuv