Orchestrate an agent team through hearing, plan, approve, and execute phases.
npx skills add https://github.com/tkgstrator/KotoTV --skill compose-tkgstrator
Please help me install this Agent Skill. Skill: compose Source: https://github.com/tkgstrator/KotoTV/tree/main/.claude/skills/compose Command: npx skills add https://github.com/tkgstrator/KotoTV --skill compose-tkgstrator
Coordinating multiple specialized agents to execute complex, cross-domain tasks without writing bespoke scripts, enabling structured collaboration and phased decision-making.
Provide a project objective and I will initiate Phase 1: Hearing with the roster and plan.
High-intent search queries and answers about installing and using this skill.
Multi-agent coordination uses a phase-driven workflow with hearing, planning, approval, and execution phases. A leader role orchestrates specialized agents across client and server domains, ensuring structured collaboration and deterministic project outcomes.
The structured workflow follows four phases: hearing for discovery, planning for task mapping, approval for validation, and execution for implementation. This process maps specialized agents to specific domains to drive deterministic outcomes.
Agent team roles are assigned through a role-based roster system that maps specialized agents to specific domains. This ensures coverage across backend, frontend, streaming, devops, and qa tasks within a structured project workflow.
Yes, a phase-driven process supports complex cross-domain agent coordination by dividing work into hearing, planning, approval, and execution phases. A leader role provides repeatable governance and prompts to ensure deterministic results.
To start a multi-agent orchestration workflow, provide a project objective to initiate Phase 1: Hearing. The system will then load the agent roster and structured plan to guide the subsequent planning and execution phases.
No, you do not need bespoke scripts to manage specialized agents. The orchestration workflow solves this problem by coordinating multiple agents through a guided governance process with predefined prompts loaded from local resources.
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