What is the main benefit of using claudemem-orchestration?

It significantly reduces investigation time by running expensive code indexing once and allowing multiple specialized agents to analyze the output in parallel, providing a 3x speedup.

Does this skill help with large codebases?

Yes, it is specifically designed for complex codebases where full AST parsing is time-consuming and requires multi-perspective validation for accuracy.

How does it handle data cleanup?

It includes a session management lifecycle with an automatic 24-hour TTL (Time-To-Live) cleanup for temporary analysis files in the /tmp directory.

What tools are required for this skill?

This skill requires claudemem and utilizes standard terminal tools like Bash and Task to manage the orchestration flow.

Which agents are supported in the role-based mapping?

It supports Architect, Developer, Tester, Debugger, and Ultrathink roles, each with specific command sets tailored to their focus area like structure, impact, or coverage.

Claudemem Multi-Agent Orchestration

Name: Claudemem Multi-Agent Orchestration
Author: tianzecn

bytianzecn

Developer Tools

Coordinates multiple AI agents to perform high-speed, parallel code analysis using shared claudemem data.

About

This skill enables efficient codebase investigation by orchestrating multiple Claude agents simultaneously. By running resource-intensive claudemem indexing commands once and sharing the output across a session-specific directory, it allows specialized agents—such as Architects, Developers, and Testers—to analyze the same codebase data from different perspectives without redundant computation. This pattern results in a 3x speedup for deep investigations and culminates in a consolidated, prioritized action plan using consensus-based analysis.

Key Features

Role-Based Command Mapping: Automatically assigns optimized claudemem commands to specific Architect, Developer, and Tester roles.
Automatic Session Cleanup: Includes a built-in lifecycle management system with a 24-hour TTL for temporary analysis data.
Parallel Execution: Launch multiple specialized agents simultaneously for significantly faster code analysis.
Consensus Analysis: Integrates findings from multiple perspectives into a single, high-confidence report.
0 GitHub stars
Shared State Management: Run expensive AST indexing once and share output files across all agents in a session.

Use Cases

Mapping complex legacy codebases to understand architecture and design patterns.
Identifying test coverage gaps and dead code across large repositories in a single pass.
Conducting multi-layered impact assessments for high-risk changes to core system symbols.

About

Key Features

Role-Based Command Mapping: Automatically assigns optimized claudemem commands to specific Architect, Developer, and Tester roles.
Automatic Session Cleanup: Includes a built-in lifecycle management system with a 24-hour TTL for temporary analysis data.
Parallel Execution: Launch multiple specialized agents simultaneously for significantly faster code analysis.
Consensus Analysis: Integrates findings from multiple perspectives into a single, high-confidence report.
0 GitHub stars
Shared State Management: Run expensive AST indexing once and share output files across all agents in a session.

Use Cases

Mapping complex legacy codebases to understand architecture and design patterns.
Identifying test coverage gaps and dead code across large repositories in a single pass.
Conducting multi-layered impact assessments for high-risk changes to core system symbols.