managing-environments
Managing Development Environments
Guidelines for working with Python virtual environments (venv) and conda environments. This skill ensures safe, organized package installations by always checking and confirming the active environment before proceeding.
Supporting files in this directory:
installation-patterns.md- Installation commands for venv and conda, channel priority, TOS error handlingbest-practices-and-scenarios.md- Common scenarios, best practices, resumable data fetch patternstroubleshooting-and-examples.md- Troubleshooting common issues and worked examples
When to Use This Skill
Activate this skill whenever:
- Installing Python packages or tools
- User requests to install dependencies
- Setting up a new Python project
- Debugging import or package issues
- Working with any Python development
Core Principles
More from delphine-l/claude_global
token-efficiency
Token optimization best practices for cost-effective Claude Code usage. Automatically applies efficient file reading, command execution, and output handling strategies. Includes model selection guidance (Opus for learning, Sonnet for development/debugging). Prefers bash commands over reading files.
1.2Kbioinformatics-fundamentals
Core bioinformatics concepts including SAM/BAM format, AGP genome assembly format, sequencing technologies (Hi-C, HiFi, Illumina), quality metrics, and common data processing patterns. Essential for debugging alignment, filtering, pairing issues, and AGP coordinate validation.
217folder-organization
Best practices for organizing project folders, file naming conventions, and directory structure standards for research and development projects
117obsidian
Integration with Obsidian vault for managing notes, tasks, and knowledge when working with Claude. Supports adding notes, creating tasks, and organizing project documentation. Updated with 2025-2026 best practices including MOCs, properties, practical organization patterns, and Obsidian CLI (1.12+).
72jupyter-notebook-analysis
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations. Includes Claude API image size helpers.
63claude-collaboration
Best practices for using Claude Code in team environments. Covers skill management, knowledge capture, version control, and collaborative workflows.
49