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Found 15 Skills
Best practices for managing development environments including Python venv and conda. Always check environment status before installations and confirm with user before proceeding.
Python environment management with venv, Poetry, Pipenv, pyenv, and conda. Use when user asks to "create virtual environment", "set up Poetry", "manage Python versions", "fix pip issues", "install dependencies", "create requirements.txt", or any Python environment tasks.
Before running Python scripts or installing packages, check for existing virtual environments and reuse them if found. If no virtual environment exists, ask the user to choose: (1) Create new venv in current directory (recommended), (2) Use system Python directly, or (3) Create venv at custom path. This applies to: running .py files, using pip/uv pip install, or any task requiring third-party packages. Exceptions: simple one-liners using only Python standard library.
Configure environment via mise [env] SSoT. TRIGGERS - mise env, mise.toml, environment variables, centralize config, Python venv, mise templates, hub-spoke architecture, monorepo structure, subfolder mise.toml.
Validate code changes by intelligently selecting and running the appropriate test suites. Use this when editing code to verify changes work correctly, run tests, validate functionality, or check for regressions. Automatically discovers affected test suites, selects the minimal set of venvs needed for validation, and handles test execution with Docker services as needed.
Fast Python environment management with uv (10-100x faster than pip). Triggers on: uv, venv, pip, pyproject, python environment, install package, dependencies.
Manage and troubleshoot PATH configuration in zsh. Use when adding tools to PATH (bun, nvm, Python venv, cargo, go), diagnosing "command not found" errors, validating PATH entries, or organizing shell configuration in .zshrc and .zshrc.local files.
Runs ML experiments reproducibly — single runs or autonomous BFS batches. Single mode: isolated venv, time-budgeted, failure-handled, logs to RESEARCH.md. BFS mode (opt-in): designs N hypotheses, runs each for a fixed budget, compares via a single verifiable metric, keeps improvements and git-resets failures — fully autonomous until done. Respects the RESEARCH.md supervision policy for notifications, approvals, and stop limits. Trigger phrases: "run experiment", "train model", "explore design space", "find best config", "autoresearch".
Uses the uv Python package and project manager correctly for dependencies, venvs, and scripts. Use when creating or modifying Python projects, adding dependencies, running scripts with inline deps, managing virtual environments, pinning Python versions, running CLI tools from PyPI, setting the IDE Python interpreter, or using uv in CI (e.g. GitHub Actions) or Docker containers. Use when the user mentions uv, pyproject.toml, uv.lock, uv run, uv add, uv sync, .venv, Python interpreter, poetry, pipenv, conda, CI, Docker, GitHub Actions, or asks to use uv instead of pip or poetry.
Build Docker images for Python services following team conventions. Use this skill when writing Dockerfiles, authoring CI image build pipelines, or adding a new service — covers mitodl image naming, git short-ref tags, relocatable uv venvs, and shared library handling.
Run Python scripts with uv including inline dependencies (PEP 723), temporary dependencies (--with), and ephemeral tool execution. Use when running scripts, needing one-off dependencies, or creating executable Python scripts. No venv activation required.
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.