venv-manager
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
Best use case
venv-manager is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
Teams using venv-manager should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/venv-manager/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How venv-manager Compares
| Feature / Agent | venv-manager | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Virtual Environment Manager Skill ## Purpose Single responsibility: Create and manage Python virtual environments for project isolation. (BP-4) ## Grounding Checkpoint (Archetype 1 Mitigation) Before executing, VERIFY: - [ ] Python interpreter available and version known - [ ] Target directory exists and is writable - [ ] No conflicting venv already active - [ ] requirements.txt or pyproject.toml exists (for install) **DO NOT create venv without confirming Python version.** ## Uncertainty Escalation (Archetype 2 Mitigation) ASK USER instead of guessing when: - Multiple Python versions available - which to use? - Existing venv found - replace or use? - requirements.txt vs pyproject.toml both present - Development vs production dependencies **NEVER delete existing venv without confirmation.** ## Context Scope (Archetype 3 Mitigation) | Context Type | Included | Excluded | |--------------|----------|----------| | RELEVANT | Python version, project deps, venv path | Application code | | PERIPHERAL | CI/CD requirements | Test configurations | | DISTRACTOR | Deployment configs | Other language envs | ## Workflow Steps ### Step 1: Check Python Environment (Grounding) ```bash # Available Python versions which python3 python3 --version # Check for existing venv ls -la venv/ 2>/dev/null || ls -la .venv/ 2>/dev/null || echo "No venv found" # Current active environment echo "VIRTUAL_ENV: $VIRTUAL_ENV" ``` ### Step 2: Create Virtual Environment ```bash # Standard creation python3 -m venv venv # With specific Python version python3.11 -m venv venv # With system packages access python3 -m venv venv --system-site-packages # Verify creation ls -la venv/bin/python ``` ### Step 3: Activate and Install Dependencies ```bash # Activate source venv/bin/activate # Upgrade pip pip install --upgrade pip # Install from requirements.txt pip install -r requirements.txt # Or from pyproject.toml pip install -e . # Development dependencies pip install -r requirements-dev.txt ``` ### Step 4: Validate Environment ```bash # List installed packages pip list # Check for missing deps pip check # Export current state pip freeze > requirements-lock.txt ``` ## Recovery Protocol (Archetype 4 Mitigation) On error: 1. **PAUSE** - Don't delete corrupted venv 2. **DIAGNOSE** - Check error type: - `venv creation failed` → Check Python installation - `pip install failed` → Check network, package availability - `activation failed` → Check shell compatibility - `dependency conflict` → Use pip-tools or poetry 3. **ADAPT** - Try alternative approach (rebuild, different Python) 4. **RETRY** - With fresh venv (max 3 attempts) 5. **ESCALATE** - Report with pip debug info **Rollback command:** ```bash rm -rf venv && python3 -m venv venv ``` ## Checkpoint Support State saved to: `.aiwg/working/checkpoints/venv-manager/` ``` checkpoints/venv-manager/ ├── python_version.txt # Python used ├── pip_freeze.txt # Installed packages ├── creation_log.txt # Creation output └── validation_status.json # Health check results ``` ## Common Commands | Command | Purpose | |---------|---------| | `python3 -m venv venv` | Create venv | | `source venv/bin/activate` | Activate (bash/zsh) | | `venv\Scripts\activate` | Activate (Windows) | | `deactivate` | Deactivate | | `pip freeze > requirements.txt` | Export deps | | `pip install -r requirements.txt` | Install deps | ## Best Practices 1. **Naming**: Use `venv/` or `.venv/` consistently 2. **Git**: Add venv to `.gitignore` 3. **Lock files**: Use `pip freeze` or `pip-tools` 4. **Isolation**: Always create project-specific venvs 5. **Documentation**: Document Python version in README ## References - Python venv docs: https://docs.python.org/3/library/venv.html - REF-001: Production-Grade Agentic Workflows (BP-4) - REF-002: LLM Failure Modes (Archetype 1 grounding)
Related Skills
aiwg-orchestrate
Route structured artifact work to AIWG workflows via MCP with zero parent context cost
pytest-runner
Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.
vitest-runner
Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.
eslint-checker
Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.
repo-analyzer
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
pr-reviewer
Review GitHub pull requests for code quality, security, and best practices. Use for automated PR feedback and approval workflows.
YouTube Acquisition
yt-dlp patterns for acquiring content from YouTube and video platforms
Quality Filtering
Accept/reject logic and quality scoring heuristics for media content
Provenance Tracking
W3C PROV-O patterns for tracking media derivation chains and production history
Metadata Tagging
opustags and ffmpeg patterns for applying metadata to audio and video files
Audio Extraction
ffmpeg patterns for extracting audio from video files and transcoding between formats
Archive Acquisition
Patterns for acquiring content from Internet Archive and archival sources