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File system operations, process management, terminal interaction, and workspace exploration
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Based on SOC occupation classification
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Showing SKILL.md
| name | mcp-desktop-commander |
| description | File system operations, process management, terminal interaction, and workspace exploration |
If MCP server desktop-commander is available, use this tool for file system and terminal operations.
Use desktop-commander for:
# List directory contents
list_directory(path, depth=2)
# Read files
read_file(path, offset=0, length=100)
# Write files
write_file(path, content, mode="rewrite" | "append")
# Create directories
create_directory(path)
# Start process
start_process(command, timeout_ms=120000, shell="powershell")
# Read process output
read_process_output(pid, offset=0, length=100)
# Send input to process
interact_with_process(pid, input="command", timeout_ms=8000)
# Terminate process
force_terminate(pid)
# Start file search
start_search(path, pattern, search_type="files" | "content")
# Get search results
get_more_search_results(session_id, offset=0, length=100)
# Stop search
stop_search(session_id)
Create, style, export, and validate publication-quality scientific and technical figures. xy is the preferred default (matplotlib.pyplot-compatible for easy migration); matplotlib/seaborn/plotly also supported. Use when the user asks for a "publication figure", "paper figure", "journal-ready plot", "scientific visualization", "SVG/PDF/TIFF export", "colorblind-safe palette", "multi-panel layout", "error bars / significance markers", or works with figure scripts that import matplotlib, seaborn, plotly, or xy.
Guide for creating, validating, improving, and benchmarking skills. Use when users want to create a skill from scratch, edit or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Generate comprehensive database schemas with proper relations, migrations, and ORM/ODM models for PostgreSQL, MongoDB, and SQLite. Use when creating database schemas that integrate with FastAPI applications, including SQLAlchemy models for SQL databases, PyMongo/ODMantic models for MongoDB, Alembic migrations, and proper relationship definitions.