What is the Parallel Claude Coordinator (PCC)?

PCC is a specialized skill for Claude Code that enables an orchestrator-agent workflow, allowing Claude to manage multiple sub-agents to complete complex tasks in parallel.

Which Claude models does the PCC skill use?

It primarily uses Claude 3.5 Sonnet for cost-efficient scouting and Claude 3 Opus for high-reasoning orchestration and implementation tasks.

How does PCC handle codebase exploration?

It spawns up to 6 Sonnet 'Scout' agents to simultaneously analyze project architecture, dependencies, and patterns before any implementation begins.

Does PCC require user approval for changes?

Yes, PCC creates a detailed Markdown plan at .claude/plans/ and waits for explicit user confirmation before deploying implementation agents.

Parallel Claude Coordinator (PCC)

Name: Parallel Claude Coordinator (PCC)
Author: Kasempiternal

byKasempiternal

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Productivity & Workflow

Coordinates a fleet of specialized Claude agents to explore, plan, and execute complex multi-file codebase modifications in parallel.

About

The Parallel Claude Coordinator (PCC) transforms Claude into a high-level project orchestrator designed for large-scale development tasks. Instead of manual file-by-file editing, PCC utilizes a sophisticated multi-agent system where Claude Opus acts as the 'Brain,' delegating tasks to multiple Sonnet 'Scouts' for exploration and Opus 'Implementers' for coding. This structured workflow includes deep codebase scouting, automated planning with user sign-off, parallel execution across multiple modules, and a final code simplification pass. It is the ideal tool for architectural refactors and implementing cross-cutting features where speed and coordination are paramount.

Key Features

Dynamic agent scaling that adjusts from 2 to 6 agents based on task complexity
10 GitHub stars
Structured implementation planning with mandatory user review and approval phases
Dual-model optimization using Opus for complex logic and Sonnet for efficient scouting
Multi-agent parallel execution for rapid codebase exploration and implementation
Integrated code simplification phase to ensure maintainability and style consistency

Use Cases

Executing large-scale architectural refactors involving many interconnected files
Implementing complex features that require simultaneous updates across frontend, backend, and tests
Rapidly mapping and understanding unfamiliar codebases through parallel scout agents

About

Key Features

Dynamic agent scaling that adjusts from 2 to 6 agents based on task complexity
10 GitHub stars
Structured implementation planning with mandatory user review and approval phases
Dual-model optimization using Opus for complex logic and Sonnet for efficient scouting
Multi-agent parallel execution for rapid codebase exploration and implementation
Integrated code simplification phase to ensure maintainability and style consistency

Use Cases

Executing large-scale architectural refactors involving many interconnected files
Implementing complex features that require simultaneous updates across frontend, backend, and tests
Rapidly mapping and understanding unfamiliar codebases through parallel scout agents