← Back to Skills Marketplace
RAGLite
by
Viraj Sanghvi
· GitHub ↗
· v1.0.0
1579
Downloads
0
Stars
1
Active Installs
1
Versions
Install in OpenClaw
/install raglite-local-rag-cache
Description
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword).
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
Purpose & Capability
The skill's stated purpose (local-first RAG cache using Chroma + ripgrep) matches the files and scripts. However the runtime intentionally defaults to the external OpenClaw engine unless the user overrides it, which conflicts with a purely 'local-first' expectation; the SKILL.md does mention the default but the install/script behavior enforces it silently.
Instruction Scope
Runtime instructions and scripts create a venv and invoke 'raglite' from the installed package. The launcher script silently injects '--engine openclaw' when the user doesn't supply --engine, which can cause documents or queries to be sent to an OpenClaw gateway by default. SKILL.md references Chroma and ripgrep and instructs interacting with network endpoints (Chroma server, OpenClaw gateway) — these are within the tool's domain, but the automatic defaulting to an external engine is behavior users may not expect and could lead to unintended data transmission.
Install Mechanism
The install script uses pip to install directly from a personal GitHub repo via 'git+https://github.com/VirajSanghvi1/raglite.git@main'. This is a common pattern but higher risk than installing from a pinned release or well-known package index: it pulls code from an upstream main branch (not a fixed tag), so upstream changes could alter behavior after install. No other unusual downloads or obfuscated installers were found.
Credentials
The skill declares no required env vars, yet SKILL.md references OPENCLAW_GATEWAY_TOKEN (used if the gateway requires auth) and a Chroma URL. Because the launcher defaults to the OpenClaw engine, an external gateway and its token become relevant to normal runs even though they are not declared as required. That mismatch makes credential use/need non-obvious to users and increases the risk of accidental exposure of sensitive documents.
Persistence & Privilege
The skill is not always-enabled, does not request system-wide config paths or credentials, and does not modify other skills. It installs into a skill-local virtualenv, which is a contained install pattern.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install raglite-local-rag-cache - After installation, invoke the skill by name or use
/raglite-local-rag-cache - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release
Metadata
Frequently Asked Questions
What is RAGLite?
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword). It is an AI Agent Skill for Claude Code / OpenClaw, with 1579 downloads so far.
How do I install RAGLite?
Run "/install raglite-local-rag-cache" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is RAGLite free?
Yes, RAGLite is completely free (open-source). You can download, install and use it at no cost.
Which platforms does RAGLite support?
RAGLite is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux).
Who created RAGLite?
It is built and maintained by Viraj Sanghvi (@virajsanghvi1); the current version is v1.0.0.
More Skills