RAGLite

Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword).

Before installing: 1) Be aware the skill will, by default, use the OpenClaw engine unless you explicitly pass --engine; that may send data to an external gateway. If you want purely local operation, always pass an explicit local engine and/or verify raglite's defaults. 2) The installer pulls from a personal GitHub 'main' branch (un-pinned); review the upstream repo or pin a specific tag/commit to avoid unexpected updates. 3) If you must keep data local, ensure OPENCLAW_GATEWAY_TOKEN is not set and run with a local Chroma instance; install and run in an isolated environment (container or VM) first. 4) Consider inspecting the installed raglite package source after installation (or vendor it) to confirm there are no unexpected network endpoints. If you are not comfortable reviewing the upstream repo or exposing data to an external gateway, treat this skill as potentially risky.

Type: OpenClaw Skill Name: raglite-local-rag-cache Version: 1.0.0 The skill installs its primary dependency, 'raglite', directly from a GitHub repository (`git+https://github.com/VirajSanghvi1/raglite.git@main`) within `scripts/install.sh`. While this is a common practice for open-source projects, it introduces a supply chain risk, as the integrity of the remote repository is not guaranteed and could be compromised to deliver malicious code. This constitutes a risky capability without clear malicious intent from the skill bundle itself, classifying it as suspicious rather than malicious under the given threshold.