An index of Claude Code workspace repositories used for system administration tasks. This documents a pattern I've identified and implemented for using Claude Code beyond traditional software development.
Note: "Claude Spaces" is my own terminology, not official Anthropic terminology.
Claude Spaces are Claude Code workspaces structured as repositories, where the primary use case is not traditional code development or editing. Instead, they leverage repository/folder structure to tightly modularize project context and data for operational, administrative, or creative workflows.
The pattern involves organizing workspaces around specific domains (desktop management, network administration, content creation, etc.) rather than software projects. This approach adapts version control and workspace organization for non-development use cases.
Each workspace typically includes:
- Context: Domain-specific information and configurations
- Memory: Historical interactions and learned patterns
- History: Documentation of actions and decisions
This repository catalogs Claude Spaces I've created, organized into two categories:
Workspaces for administering local computers (developed for Linux desktops).
| Repository | Scope | Description | Companion Resources |
|---|---|---|---|
| Claude Linux Desktop Manager |  |
Desktop environment management (GUI WIP) | Slash Commands CLAUDE.md Seeder |
| Home Folder Claude MD |  |
Home directory administration | - |
| Claude Code Repo Managers ClaudeMD |  |
Repository base level management | - |
| Repository | Task Type | Description |
|---|---|---|
| Claude Conda Manager |  |
Conda environment management |
| Claude Code Security Auditor |  |
Security auditing workspace |
Workspaces for administering devices on local networks or remote systems over SSH.
| Repository | Protocol/Scope | Description |
|---|---|---|
| Claude Code Remote Machine Admin Space |  |
Individual remote system administration |
| Claude Code LAN Manager |  |
Homelab and collective remote management |
Current implementations use Claude Code for:
- System configuration and optimization
- Package and dependency management
- Network administration
- Security auditing and hardening
- Environment setup and maintenance
- Multi-system orchestration
- Infrastructure documentation
The pattern could extend to other domains beyond system administration. The approach of maintaining persistent context, history, and system prompts in workspace repositories could apply to various operational workflows.
See iteration.md for a comprehensive list of potential applications across content creation, research, business operations, education, and more.
| Space Name | Category | Description |
|---|---|---|
| Claude Video Processing Space |  |
Video editing, transcoding, and manipulation workflows using ffmpeg, MLT, and other CLI tools |
| Claude Audio Processing Space |  |
Audio engineering, podcast production, music processing with Sox, ffmpeg, and audio libraries |
| Claude Image Workspace |  |
Batch image processing, optimization, format conversion, and computer vision tasks |
| Space Name | Category | Description |
|---|---|---|
| Claude Data Pipeline Space |  |
ETL workflows, data transformation, and analytics automation |
| Claude Database Admin Space |  |
Database maintenance, backup orchestration, query optimization, and schema management |
| Space Name | Category | Description |
|---|---|---|
| Claude Documentation Generator Space |  |
Technical writing, API documentation generation, and knowledge base management |
| Claude Content Publishing Space |  |
Blog management, static site generation, content pipeline automation |
| Space Name | Category | Description |
|---|---|---|
| Claude Container Orchestration Space |  |
Docker/Podman management, container lifecycle, and registry operations |
| Claude CI/CD Pipeline Space |  |
Build automation, testing workflows, and deployment orchestration |
| Claude Backup & Recovery Space |  |
Backup strategy management, disaster recovery planning, and restoration workflows |
| Space Name | Category | Description |
|---|---|---|
| Claude Deep Research Space |  |
Task-specific research workspaces with accumulated context, sources, and analytical threads |
| Claude ML Experiment Space |  |
Machine learning experiment tracking, model versioning, and hyperparameter management |
| Claude Scientific Computing Space |  |
HPC job management, computational workflows, and research data organization |
| Space Name | Category | Description |
|---|---|---|
| Claude Monitoring Space |  |
Log aggregation, metrics collection, alerting configuration, and observability dashboards |
Any domain involving systematic workflows and accumulated context could potentially benefit from this workspace pattern.
The defining characteristic: using repository/folder structure to tightly modularize project context and data for non-traditional code development use cases.
This approach adapts Claude Code for operational, administrative, and creative tasks by maintaining persistent workspaces with:
- Repository-based organization leveraging version control for context management
- Accumulated context specific to the domain
- Historical records of actions and decisions
- System prompts tailored to the workflow
- Documentation that evolves with use
The pattern makes complex operational tasks more repeatable and better documented by treating them as ongoing collaborative processes rather than isolated sessions—all within the familiar structure of a repository, but without the focus on code editing or software development.
While this repository demonstrates the pattern using Claude Code, this is fundamentally an AI agent CLI + repository pattern that can be implemented with any AI agent wrapper or CLI tool.
The core insight is that modularizing context, history, MCP servers, and configurations into dedicated repositories provides significant benefits for non-development tasks:
- Context Persistence: Repository structure maintains domain-specific context across sessions
- History Tracking: Version control naturally documents the evolution of configurations and decisions
- Modular Configuration: Each workspace can have tailored system prompts, slash commands, and MCP servers
- Reproducibility: Workspace repositories can be cloned, shared, or restored
- Organization: Clear separation of concerns across different operational domains
This pattern could equally be implemented with:
- Qwen CLI/Wrapper: Using Qwen's command-line interface with repository-based workspaces
- Goose: Duck AI's agent system with folder-based context management
- Codex Wrappers: OpenAI Codex CLIs with persistent workspace configurations
- Aider: AI pair programming tool with project-specific context
- Continue: VS Code extension with workspace-level customization
- Custom Agent Implementations: Any AI agent system that can work with file-based context and configuration
Any AI agent CLI can adopt this pattern if it supports:
- Working Directory Context: Ability to operate within a specific folder/repository
- Configuration Files: Support for workspace-specific settings (e.g., CLAUDE.md, system prompts, slash commands)
- Persistent Memory: Mechanism to maintain context across sessions (files, databases, etc.)
- Tool/MCP Integration: Extensibility through plugins, tools, or Model Context Protocol servers
The specific implementation (Claude Code, Qwen, Goose, etc.) is simply the execution layer—the real value comes from the repository-based modularization of operational workflows.
This repository documents my implementation using Claude Code, but the underlying pattern is tool-agnostic and represents a broader approach to AI-assisted operational work.