Tencent SkillHub · Productivity

RAGLite - Local Expandable Library AI Library

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

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

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: scripts/install.sh, scripts/openclaw.plugin.json, scripts/raglite.sh, SKILL.md, manifest.json

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

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New install

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: VirajSanghvi1
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 13 sections Open source page

RAGLite — a local RAG cache (not a memory replacement)

RAGLite is a local-first RAG cache. It does not replace model memory or chat context. It gives your agent a durable place to store and retrieve information the model wasn’t trained on — especially useful for local/private knowledge (school work, personal notes, medical records, internal runbooks).

Why it’s better than “paid RAG” / knowledge bases (for many use cases)

Local-first privacy: keep sensitive data on your machine/network. Open-source building blocks: Chroma 🧠 + ripgrep ⚡ — no managed vector DB required. Compression-before-embeddings: distill first → less fluff/duplication → cheaper prompts + more reliable retrieval. Auditable artifacts: the distilled Markdown is human-readable and version-controllable. If you later outgrow local, you can swap in a hosted DB — but you often don’t need to.

1) Condense ✍️

Turns docs into structured Markdown outputs (low fluff, more “what matters”).

2) Index 🧠

Embeds the distilled outputs into a Chroma collection (one DB, many collections).

3) Query 🔎

Hybrid retrieval: vector similarity via Chroma keyword matches via ripgrep (rg)

Default engine

This skill defaults to OpenClaw 🦞 for condensation unless you pass --engine explicitly.

Prereqs

Python 3.11+ For indexing/query: Chroma server reachable (default http://127.0.0.1:8100) For hybrid keyword search: rg installed (brew install ripgrep) For OpenClaw engine: OpenClaw Gateway /v1/responses reachable OPENCLAW_GATEWAY_TOKEN set if your gateway requires auth

Install (skill runtime)

This skill installs RAGLite into a skill-local venv: ./scripts/install.sh It installs from GitHub: git+https://github.com/VirajSanghvi1/raglite.git@main

One-command pipeline (recommended)

./scripts/raglite.sh run /path/to/docs \ --out ./raglite_out \ --collection my-docs \ --chroma-url http://127.0.0.1:8100 \ --skip-existing \ --skip-indexed \ --nodes

Query

./scripts/raglite.sh query ./raglite_out \ --collection my-docs \ --top-k 5 \ --keyword-top-k 5 \ "rollback procedure"

Outputs (what gets written)

In --out you’ll see: *.tool-summary.md *.execution-notes.md optional: *.outline.md optional: */nodes/*.md plus per-doc *.index.md and a root index.md metadata in .raglite/ (cache, run stats, errors)

Troubleshooting

Chroma not reachable → check --chroma-url, and that Chroma is running. No keyword results → install ripgrep (rg --version). OpenClaw engine errors → ensure gateway is up and token env var is set.

Pitch (for ClawHub listing)

RAGLite is a local RAG cache for repeated lookups. When you (or your agent) keep re-searching for the same non-training data — local notes, school work, medical records, internal docs — RAGLite gives you a private, auditable library: Distill to structured Markdown (compression-before-embeddings) Index locally into Chroma Query with hybrid retrieval (vector + keyword) It doesn’t replace memory/context — it’s the place to store what you need again.

Category context

Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Scripts2 Config1 Docs

SKILL.md Primary doc
scripts/install.sh Scripts
scripts/raglite.sh Scripts
manifest.json Config
scripts/openclaw.plugin.json Config