swarm-coordination

Coordinate multi-agent swarms in OpenCode with delegation and dependency graphs.

Updated Feb 5, 2026

Search Tags:#monitoring #delegation #multi-agent #swarm #coordination #opencode #progress

Ready to run

npx skills add https://github.com/Khoiwall/be_vidify --skill swarm-coordination-khoiwall

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swarm-coordination
Source: https://github.com/Khoiwall/be_vidify/tree/main/.opencode/skill/swarm-coordination
Command: npx skills add https://github.com/Khoiwall/be_vidify --skill swarm-coordination-khoiwall

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of orchestrating multiple autonomous tasks that can run in parallel by providing a structured framework for leaders and workers, delegation, progress tracking, and cross-session recovery.

Core Features & Use Cases

Swarm orchestration: leader spawns and monitors worker agents to execute independent tasks
Progress tracking and persistence: real-time UI and durable state across sessions
Dependency-aware scheduling: groups tasks by parallelizable cohorts based on a dependency graph
Beads integration: tie swarm status back to OpenCode beads for recovery

Quick Start

Instantiate a swarm workflow by analyzing a user task, push tasks to Beads, spawn workers in dependency waves, monitor progress, verify results, and gracefully close the swarm.

Frequently Asked Questions about swarm-coordination

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I coordinate multi-agent swarms for parallel execution in OpenCode?▼

Multi-agent swarm coordination is managed by spawning leader and worker agents that execute independent subtasks in parallel. The system enforces delegation packets and dependency graphs to schedule tasks in parallelizable cohorts, ensuring reliable cross-session execution.

What is dependency-aware scheduling for multi-agent workflows?▼

Dependency-aware scheduling groups tasks into parallelizable cohorts based on a dependency graph. This mechanism allows leader agents to spawn worker waves sequentially, ensuring independent subtasks run concurrently while respecting execution order and cross-session recovery constraints.

Can I recover swarm execution state across sessions if a worker agent fails?▼

Cross-session recovery is supported by tying durable swarm state back to OpenCode beads. Progress reporting and persistence mechanisms track worker execution status, allowing the leader agent to verify results and resume incomplete subtasks after session interruptions.

How do I track real-time progress and status of delegated worker agents?▼

Worker agent progress is tracked through enforced progress reporting integrated with the OpenCode UI. The leader monitors delegation packets and durable state updates, providing real-time visibility into parallel subtask execution and enabling graceful shutdown upon completion.

When should I use parallel swarm orchestration instead of sequential task execution?▼

Parallel swarm orchestration is ideal for tasks containing multiple independent subtasks that require concurrent processing. If your workflow involves complex dependency graphs needing cross-session recovery and coordinated delegation, swarms outperform sequential execution.

Do I need specific dependencies to run multi-agent swarms using Kimi K2.5 PARL patterns?▼

No external dependencies are required to implement Kimi K2.5 PARL patterns for multi-agent swarms. The skill operates natively within OpenCode to handle leader-worker delegation, dependency graphs, and progress reporting without additional component installations.