Does the orchestrator make changes directly?

No, the orchestrator acts as the 'brain,' focusing on delegation, synthesis, and planning, while specialized sub-agents handle the direct coding work.

Can I review the implementation strategy before coding begins?

Yes, the skill includes a mandatory plan creation and user approval phase where you can review and edit the strategy before any implementation agents are spawned.

Why use Opus for both scouts and implementers?

Using Opus for both phases ensures that the reasoning depth used to understand the architecture matches the intelligence used to implement changes, reducing logic errors in complex systems.

How does the skill ensure code quality after parallel implementation?

The workflow includes a dedicated verification phase with test runners and a parallel code simplification phase to ensure consistency, readability, and maintainability.

How many agents can this skill spawn simultaneously?

The system dynamically scales between 2 and 6 agents per phase—exploration, implementation, and simplification—based on the complexity of the task.

PCC-Opus Orchestrator

Name: PCC-Opus Orchestrator
Author: Kasempiternal

byKasempiternal

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开发者工具

Orchestrates a parallel multi-agent workflow using Claude 3 Opus for high-stakes coding tasks requiring deep architectural reasoning and systematic exploration.

关于

PCC-Opus is a specialized orchestrator skill designed for critical software engineering tasks where quality and reasoning depth are paramount. It transforms Claude into a project lead that coordinates multiple Opus-powered 'scouts' and 'implementers' in parallel. Instead of making manual code changes, the orchestrator systematically explores complex codebases, synthesizes findings into architectural insights, generates detailed implementation plans for user approval, and delegates work to specialized sub-agents. This multi-phase approach ensures maximum exploration quality, consistent patterns, and rigorous verification for complex refactors or system-wide features.

主要功能

Automated code simplification and verification phase for production-grade output
10 GitHub stars
Dynamic multi-agent parallelization for both codebase exploration and task implementation
Opus-to-Opus coordination ensuring maximum reasoning depth across all sub-tasks
Architectural insight synthesis from dynamic scout agents (2-6 per task)
Formal planning phase with user approval gates and risk mitigation strategies

使用场景

Complex architectural refactors across large, unfamiliar codebases
High-stakes bug fixes in critical infrastructure where reasoning quality is non-negotiable
Implementing system-wide features requiring deep dependency analysis

关于

主要功能

Automated code simplification and verification phase for production-grade output
10 GitHub stars
Dynamic multi-agent parallelization for both codebase exploration and task implementation
Opus-to-Opus coordination ensuring maximum reasoning depth across all sub-tasks
Architectural insight synthesis from dynamic scout agents (2-6 per task)
Formal planning phase with user approval gates and risk mitigation strategies

使用场景

Complex architectural refactors across large, unfamiliar codebases
High-stakes bug fixes in critical infrastructure where reasoning quality is non-negotiable
Implementing system-wide features requiring deep dependency analysis