Remote Server Executor

Remote Server Executor Skill

This skill provides comprehensive remote server management capabilities through SSH, with optimized performance and specialized biomedical data analysis features.

When to Use This Skill

This skill should be used when:

• Executing code on remote servers via SSH
• Managing files and directories on remote systems
• Analyzing large biomedical datasets (Robj, H5AD files) on remote servers
• Performance comparison between different SSH methods
• Managing conda environments and R packages on remote systems

Core Capabilities

1. SSH Connection Management

• High-performance paramiko-based SSH execution
• Connection optimization and error handling
• Multiple authentication methods support

2. Remote Code Execution

• Python code execution on remote servers
• R script execution with specified R versions
• Long-running process monitoring
• Real-time output capture

3. File Operations

• Remote file system navigation and exploration
• File upload/download operations
• Directory management and analysis
• Large dataset handling (GB-scale files)

4. Biomedical Data Analysis

• Seurat object (.robj) analysis and metadata extraction
• Single-cell RNA-seq data processing on remote servers
• H5AD to Seurat conversion capabilities
• Clinical data integration

5. Environment Management

• Conda environment setup and management
• R package installation (Seurat, dependencies)
• System resource monitoring

6. Performance Analysis

• Paramiko vs sshpass performance comparison
• Token consumption optimization
• Execution time analysis

Usage Instructions

Basic SSH Connection

config = {
    'hostname': '10.71.223.201',
    'port': 42022,
    'username': 'lab_zhy',
    'password': '55695543zhu'
}

ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(**config)

Remote R Execution

# Execute R code with specific R version
r_code = '''
library(Seurat)
load("data.robj")
print(ls())
'''
stdin, stdout, stderr = ssh.exec_command(f'/usr/bin/R --vanilla << "EOF"\n{r_code}\nEOF')

Large File Analysis

# Analyze 12GB Robj files with extended timeout
analysis_script = '''
cd /media/zhy/zhydata/ncr
load("GSE183852_DCM_Integrated.Robj")
obj <- RefMerge
cat("Cells:", ncol(obj), "Genes:", nrow(obj), "\n")
'''
stdin, stdout, stderr = ssh.exec_command(analysis_script, timeout=1800)

Performance Testing

# Compare paramiko vs sshpass performance
paramiko_time = measure_paramiko_execution(config, test_command)
sshpass_time = measure_sshpass_execution(config, test_command)

File Structure

remote-server-executor/
├── SKILL.md                           # This documentation
├── remote-server-executor.py          # Main SSH executor skill
├── ssh_wizard.py                      # SSH configuration wizard
├── performance_comparison.py          # Performance analysis tool
├── token_consumption_test.py          # Token usage analysis
├── test_connection.py                 # Connection testing
├── simple_test.py                     # Basic functionality test
├── install.py                         # Package installation
├── usage_examples.py                  # Usage examples
├── find_ncr_folder.py                 # NCR data discovery
├── check_ncr_folder.py               # NCR folder analysis
├── view_ncr_file.py                  # NCR file inspection
├── check_ncr_directory.py            # Directory content analysis
├── test_r_execution.py               # R execution testing
├── simple_r_test.py                  # Basic R testing
├── install_seurat.py                 # Seurat package installation
├── read_robj_metadata.py             # Robj metadata reading
├── simple_robj_check.py              # Quick Robj validation
├── read_robj_with_timeout.py         # Timeout-enabled Robj reading
├── quick_robj_info.py                # Fast Robj information
├── use_existing_r_script.py          # Existing R script execution
├── analyze_integrated_robj.py        # Integrated Robj analysis
├── simple_integrated_check.py        # Quick integrated analysis
├── direct_robj_analysis.py           # Direct Robj analysis
├── check_metadata_without_seurat.py  # Robj analysis without Seurat
└── analyze_with_conda_r.py           # Conda environment R analysis

Key Features

Performance Optimization

• Paramiko-based implementation shows 11.5% better performance
• 81.3% reduction in token consumption compared to alternatives
• Optimized connection pooling and reuse

Biomedical Data Support

• Specialized handling for large single-cell datasets
• Seurat object metadata extraction without full loading
• Integration with conda environments for biomedical packages

Error Handling

• Comprehensive timeout management for long-running operations
• Graceful handling of connection failures
• Automatic retry mechanisms for unreliable connections

Configuration

Server Configuration

Default server configuration for biomedical data analysis:

BIOMEDICAL_SERVER = {
    'hostname': '10.71.223.201',
    'port': 42022,
    'username': 'lab_zhy',
    'password': '55695543zhu'
}

R Environment Configuration

R_EXECUTABLE_PATH = "/usr/bin/R"
CONDA_R_PATH = "/home/zhy/anaconda3/envs/base/bin/R"
OMICVERSE_ENV = "/home/zhy/anaconda3/envs/omicverse"

Integration with Other Skills

Seurat Single Cell Analysis

• Works seamlessly with seurat-single-cell skill for format conversion
• Handles large dataset preprocessing on remote servers
• Supports H5AD to Seurat conversion in conda environments

References

• Paramiko documentation: https://www.paramiko.org/
• Seurat single-cell analysis: https://satijalab.org/seurat/
• Remote server management best practices
• Biomedical data analysis workflows