install-apeiron

name	install-apeiron
description	Install the apeiron continual-learning package as a dependency into an existing Python project so the user can `import apeiron`. Use when the user wants to add apeiron to their own project or training framework, set it up as a path/git dependency, or get `from apeiron import ...` working in another codebase. Handles Poetry presence, Python 3.13 verification, and automatic GPU-vs-CPU PyTorch selection. SKIP for developing inside THIS repo itself — that is just `poetry install`.
argument-hint	[target_project_dir] [--git <url>]
user-invocable	true
allowed-tools	["Bash","Read","Edit","Write","Glob","Grep"]

Install apeiron as a dependency in the user's own Python project, hands-off.

Arguments

• $1: Target project directory (the project that will depend on apeiron). Defaults to the current working directory.
• --git <url>: Optional. Install apeiron from this git URL instead of a local path. If omitted, prefer a local path dependency (see step 2).

Do not assume any value not given — read it from the repo or ask.

Goal

After this skill runs, the following must succeed from inside the target project's environment:

python -c "from apeiron import BaseModelHarness, ContinuousMonitor, build_config; print('apeiron OK')"

Procedure

1. Resolve the target project and its package manager

• Target dir = $1 or the current working directory. Confirm it contains a pyproject.toml (Poetry/PEP 621) or requirements.txt/setup.py (pip). If none, ask the user how they manage dependencies.
• Detect the manager: Poetry if [tool.poetry] or poetry.lock is present; otherwise pip/uv. Poetry is the primary path below; a pip fallback is in step 6.

2. Resolve the apeiron source (do not hardcode versions or paths)

• If --git <url> was given, use it as a git dependency.
• Else look for a local apeiron checkout. The current repo IS apeiron when its pyproject.toml has name = "apeiron". Confirm with:

  grep -m1 'name = "apeiron"' pyproject.toml && pwd
  

  Use that absolute path as a path (develop) dependency. If the current repo is not apeiron and no --git was given, ask the user for the apeiron path or git URL.

3. Verify Python (guide, don't auto-manage interpreters)

• Read apeiron's required range dynamically rather than assuming it:

  grep 'requires-python' <apeiron_pyproject>
  

• Check the interpreter the target project will use (python --version, or poetry env info --python). If it is outside the range, stop and give the user exact instructions (e.g. install the matching CPython via pyenv/uv and point Poetry at it with poetry env use <path>). Do not silently install or switch interpreters.

4. Ensure Poetry is available (auto-install if missing)

• command -v poetry — if missing, install it: pipx install poetry (preferred) or pip install --user poetry, then re-check poetry --version.

5. Detect compute backend and select the PyTorch wheel

• Probe for an NVIDIA GPU:

  nvidia-smi -L 2>/dev/null && echo "GPU_PRESENT" || echo "NO_GPU"
  

• GPU present: do nothing special — the default CUDA-enabled torch wheels resolve normally. Report that CUDA wheels will be used.
• No GPU: pin torch to the CPU-only index so the install is smaller and portable. For a Poetry target, add an explicit source and route torch to it before adding apeiron:

  [[tool.poetry.source]]
  name = "pytorch-cpu"
  url = "https://download.pytorch.org/whl/cpu"
  priority = "explicit"
  
  [tool.poetry.dependencies]
  torch = { source = "pytorch-cpu" }
  

  Then poetry lock. Report that CPU-only wheels will be used.

6. Add the dependency

From the target project directory:

• Poetry, local path: poetry add --editable <absolute_apeiron_path>
• Poetry, git: poetry add "git+<url>"
• pip/uv fallback, local path: pip install -e <absolute_apeiron_path> (for the no-GPU case, first run pip install torch --index-url https://download.pytorch.org/whl/cpu)
• pip/uv fallback, git: pip install "apeiron @ git+<url>"

7. Verify and report

• Run the import check from step Goal inside the target environment (poetry run python -c ... for Poetry).
• On success, report: the apeiron source used (path/git), Python version, compute backend chosen (CUDA/CPU), and the manager the dependency was added to.
• Suggest next steps: /run-experiment to try a bundled example, or the integration skill if they are wiring apeiron into an existing training loop.

Troubleshooting

• ModuleNotFoundError: apeiron after install — the editable/path link didn't register; re-run the add in the target project dir, not the apeiron repo.
• torch pulls CUDA wheels on a CPU box — the explicit pytorch-cpu source from step 5 was not applied before poetry lock; re-lock after adding it.
• Python version conflict — apeiron pins a narrow CPython range (see step 3); the target project must use a matching interpreter. Point Poetry at it with poetry env use.