Does this skill require manual coordination?

No, the Parallel Agent Spawner includes a built-in dependency analyzer and result aggregator that automates the coordination and merging process.

How does the Parallel Agent Spawner handle merge conflicts?

It utilizes a configurable merge strategy, supporting both sequential updates and git-style merging to resolve conflicts during the result aggregation phase.

What types of tasks are best suited for parallelization?

Independent tasks such as implementing decoupled features, running test suites, performing code reviews, and conducting codebase research are ideal.

Can I limit the number of agents running at once?

Yes, the SpawnConfig allows you to set a 'max_parallel' parameter to manage resource usage and ensure optimal API throughput.

Parallel Agent Spawner

Name: Parallel Agent Spawner
Author: adaptationio

byadaptationio

생산성 및 워크플로

Spawns and coordinates multiple autonomous agents simultaneously to accelerate software development tasks and feature implementation.

소개

The Parallel Agent Spawner is an orchestration skill designed to drastically reduce development time by distributing tasks across multiple concurrent Claude agents. By analyzing project dependencies and identifying parallelizable units of work—such as coding features, running E2E tests, or performing code reviews—it manages a pool of worker agents, handles their lifecycles, and automatically aggregates results. This skill is essential for complex projects where sequential task execution creates bottlenecks, providing robust conflict resolution and merge strategies to ensure a cohesive final output.

주요 기능

Automated dependency analysis to identify safe parallelization opportunities
0 GitHub stars
Configurable agent pooling with custom timeouts and retry logic
Integrated result aggregation with sequential or git-based merge strategies
Support for specialized agent types including coding, testing, review, and research
Multi-agent orchestration for concurrent feature development and testing

사용 사례

Performing simultaneous code reviews and codebase research for rapid architectural assessments
Running comprehensive end-to-end test suites in parallel to reduce feedback loops
Accelerating large-scale feature implementation by splitting sub-tasks across multiple agents

소개

주요 기능

Automated dependency analysis to identify safe parallelization opportunities
0 GitHub stars
Configurable agent pooling with custom timeouts and retry logic
Integrated result aggregation with sequential or git-based merge strategies
Support for specialized agent types including coding, testing, review, and research
Multi-agent orchestration for concurrent feature development and testing

사용 사례

Performing simultaneous code reviews and codebase research for rapid architectural assessments
Running comprehensive end-to-end test suites in parallel to reduce feedback loops
Accelerating large-scale feature implementation by splitting sub-tasks across multiple agents