Federate beads across multiple repositories to coordinate AI agent workflows.
npx skills add https://github.com/thrashr888/AllBeads --skill allbeads
Please help me install this Agent Skill. Skill: allbeads Source: https://github.com/thrashr888/AllBeads/tree/main/claude-plugin/skills/allbeads Command: npx skills add https://github.com/thrashr888/AllBeads --skill allbeads
Federate beads across multiple repositories to coordinate AI agent workflows and provide a unified view of cross-repo work, reducing context-switching and coordination overhead.
Initialize contexts for your repositories and start the dashboard to begin cross-repo coordination.
High-intent search queries and answers about installing and using this skill.
Multi-repo AI agent workflows are coordinated using a federated graph that unifies cross-repo tasks and handoffs. This approach applies to polyrepo architectures, providing a real-time dashboard for multi-repo visibility.
A federated graph for cross-repo orchestration is a unified structure that connects tasks across independent repositories. It uses "Shadow Beads" to tie Epics to native beads in member repositories, reducing context-switching and coordination overhead.
Task handoffs in a polyrepo architecture are handled via Agent Mail, a system for agent-to-agent messaging. These cross-repo handoffs include governance and policy enforcement to maintain workflow integrity across repositories.
Yes, cross-repo workflow management supports direct integration with JIRA and GitHub Issues. This ties external issue tracking into the federated graph, unifying governance alongside native repository contexts.
To initialize contexts and start a dashboard for multi-repo visibility, configure contexts for your target repositories. Once initialized, start the dashboard to begin cross-repo coordination and monitor the federated graph.
A federated governance framework for AI workflows is needed when managing complex cross-repo tasks requiring policy enforcement and unified visibility. It is essential for polyrepo architectures where multiple AI agents require coordinated handoffs and context management.
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