Automate multi-agent task execution with batched token-efficient processing.
npx skills add https://github.com/Miosa-osa/canopy --skill batch-miosa-osa
Please help me install this Agent Skill. Skill: /batch Source: https://github.com/Miosa-osa/canopy/tree/main/library/skills/workflow/batch Command: npx skills add https://github.com/Miosa-osa/canopy --skill batch-miosa-osa
Batch processing of tasks across multiple agents often leads to wasted tokens and coordination overhead. This Skill provides a framework to automatically analyze task complexity, plan agent batches, execute batches sequentially with parallelism within each batch, and synthesize results with an orchestrator, reducing token usage and latency.
Invoke /batch with your task string to start batching across agents.
High-intent search queries and answers about installing and using this skill.
Multi-agent orchestration with batching cuts token waste by dividing complex workflows into sequential batches of 3-5 agents. This approach executes agents in parallel within each batch while maintaining isolated context, significantly lowering coordination overhead and token consumption.
Batch planning for multi-agent orchestration involves analyzing task complexity to determine required agents, then grouping them into cohesive batches of 3-5. Batches execute sequentially with parallelism inside each batch, and an orchestrator synthesizes the logged batch results into a final output.
Batch processing supports task coordination across feature development, data processing, and QA domains. The framework analyzes complexity and sequences agent batches to handle diverse workflows, using an orchestrator to synthesize results for end-to-end delivery across these varied contexts.
The orchestrator synthesizes batch results by collecting logged outputs from sequential agent batches. After each batch of 3-5 agents executes with internal parallelism, the orchestrator processes the per-batch context to integrate and synthesize the outcomes into a cohesive final output.
Multi-agent coordination without batching leads to wasted tokens due to repetitive context passing and high coordination overhead. Batching mitigates this by analyzing task complexity, managing per-batch context, and sequencing execution to optimize batch sizes and reduce overall token usage.
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