Claude-Zeroclaw-Nexus
NewClaude Code ZeroClaw Pro 2026: AI Multitool & CLI Router for Claude SDK Subagents
Summary
Claude-Zeroclaw-Nexus is an AI multitool and CLI router that orchestrates Claude SDK subagents for complex automation workflows.
- It enables developers to chain multiple agent tasks, route commands across specialized subagents, and manage multi-step processes from a single interface.
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/claude-zeroclaw-nexus.md
@claude-zeroclaw-nexusUse Cases
Usage Examples
/claude-zeroclaw-nexus route 'fetch latest sales data, analyze trends, and email report'
/claude-zeroclaw-nexus chain 'lint code, run unit tests, build, deploy to staging'
Use Claude-Zeroclaw-Nexus to orchestrate subagents for a full CI/CD pipeline: test, build, and deploy.
Security Audits
Frequently Asked Questions
What is Claude-Zeroclaw-Nexus?
Claude-Zeroclaw-Nexus is an AI multitool and CLI router that orchestrates Claude SDK subagents for complex automation workflows. It enables developers to chain multiple agent tasks, route commands across specialized subagents, and manage multi-step processes from a single interface.
How to install Claude-Zeroclaw-Nexus?
To install Claude-Zeroclaw-Nexus: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/claude-zeroclaw-nexus.md. Finally, @claude-zeroclaw-nexus in Claude Code.
What is Claude-Zeroclaw-Nexus best for?
Claude-Zeroclaw-Nexus is a agent categorized under General. It is designed for: agent. Created by omaralisql.
What can I use Claude-Zeroclaw-Nexus for?
Claude-Zeroclaw-Nexus is useful for: Automate a multi-step data pipeline by routing ETL, analysis, and reporting tasks to separate subagents.; Deploy and manage microservices by having subagents handle build, test, and deployment steps sequentially.; Orchestrate a code review workflow where one subagent checks style, another runs tests, and a third merges approved PRs.; Route user queries to the appropriate specialized subagent (e.g., database queries, API calls, file operations) based on intent.; Chain subagents to perform research, summarize findings, and generate a report in a single command.; Simulate a team of AI assistants working together on a large software project, each handling a different module..