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Description

Enterprise-grade knowledge base agent with interactive knowledge graph. CLI-atomic, drop-in skill for Claude Code / OpenClaw. Turn any folder (Word/PDF/Excel/PPT) into a three-layer wiki: articles + concepts + vis-network graph. ~10% RAG token cost, 10× lower compile cost via smart incremental training.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

lcwiki

[English](README.md) | [简体中文](README.zh-CN.md)

[![PyPI](https://img.shields.io/pypi/v/lcwiki.svg)](https://pypi.org/project/lcwiki/) [![Python](https://img.shields.io/pypi/pyversions/lcwiki.svg)](https://pypi.org/project/lcwiki/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE) [![CI](https://github.com/LCccode/wikigraph/actions/workflows/ci.yml/badge.svg)](https://github.com/LCccode/wikigraph/actions/workflows/ci.yml)

**The enterprise-grade knowledge base agent, grounded in personal knowledge management theory, built for cross-department KB co-creation.** Type `/lcwiki` in Claude Code or OpenClaw — lcwiki plugs into your AI agent as its **long-term KB brain**, trains on any folder of docs your teams drop in, and answers cross-department queries with **precision knowledge allocation** at ~10% the token cost of vanilla RAG and **~10× lower ongoing training cost** via smart incremental ingest.

**Mount lcwiki on OpenClaw or Claude Code as a drop-in agent brain.** One `pip install`, one `lcwiki install --platform claude|claw`, and your AI agent has a persistent, structured, collaboratively-trained knowledge base it can query accurately — across sessions, across departments, across months of KB drift.

Fully multimodal. Drop in `.docx`, `.pdf`, `.xlsx`, `.pptx`, markdown, images, audio, or video — lcwiki converts everything to markdown, extracts per-doc structure, concepts with cross-department family aliases, and an interactive knowledge graph in one shot. Then it lets your AI query the KB with a three-layer token-first fallback: scan 100-token tldrs → fall back to article body → only touch raw content as a last resort.

📖 The positioning

lcwiki sits at the intersection of three traditions that rarely talk to each other:

  1. Personal Knowledge Management (PKM) theory — Niklas Luhmann's Zettelkasten, Tiago Forte's Building a Second Brain, the Obsidian / Logseq / Roam community: atomic notes, bidirectional links,