llm-wiki-newsroom
NewAn LLM-maintained knowledge wiki run by a multi-agent "newsroom" — local-first, API-key-free, a structured alternative to RAG.
Summary
This skill sets up a local-first, multi-agent knowledge wiki that is maintained by an LLM-powered 'newsroom', providing a structured alternative to RAG without requiring any API keys.
- It enables developers to collaboratively build and update a persistent knowledge base through automated agent workflows.
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/llm-wiki-newsroom.md
@llm-wiki-newsroomUse Cases
Usage Examples
/llm-wiki-newsroom add entry: 'The authentication module uses JWT tokens with a 24-hour expiry.'
/llm-wiki-newsroom query: 'What is the current architecture for the payment service?'
/llm-wiki-newsroom summarize: 'Summarize today's standup notes and add them to the wiki.'
Security Audits
Frequently Asked Questions
What is llm-wiki-newsroom?
This skill sets up a local-first, multi-agent knowledge wiki that is maintained by an LLM-powered 'newsroom', providing a structured alternative to RAG without requiring any API keys. It enables developers to collaboratively build and update a persistent knowledge base through automated agent workflows.
How to install llm-wiki-newsroom?
To install llm-wiki-newsroom: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/llm-wiki-newsroom.md. Finally, @llm-wiki-newsroom in Claude Code.
What is llm-wiki-newsroom best for?
llm-wiki-newsroom is a agent categorized under General. It is designed for: api, agent. Created by alfadur7.
What can I use llm-wiki-newsroom for?
llm-wiki-newsroom is useful for: Automatically curate and update a team wiki from chat logs and documents without manual editing.; Maintain a living knowledge base of project decisions, architecture notes, and code conventions.; Run a newsroom-style agent that summarizes and archives daily standup notes and meeting minutes.; Create a local-first wiki for personal research notes that evolves with new information.; Use the multi-agent system to fact-check and cross-reference entries in the wiki.; Replace traditional RAG pipelines with a structured, agent-maintained wiki for better context retrieval..