Container Layer

Build a reproducible, cached environment overlay for ephemeral containers using a Dockerfile-like spec.

Concept

The container resets every session, but your environment shouldn't. This skill:

1. Parses a Containerfile (Dockerfile subset) that declares your environment
2. Caches the built result as a tarball in GitHub Releases
3. Restores from cache on subsequent boots (single fetch vs. N installs)
4. Provides a uv shim that captures ad-hoc installs back into the Containerfile

Supported Containerfile Instructions

# Environment variables
ENV KEY=value

# Shell commands (including package installs)
RUN apt-get install -y foo        # system packages
RUN uv pip install pandas numpy   # Python packages (preferred)
RUN pip install requests          # also works

# Fetch files from URLs or GitHub
FETCH https://example.com/file.tar.gz /dest/path
FETCH github:user/repo /dest/path              # latest tarball
FETCH github:user/repo@ref /dest/path          # specific ref

# Set working directory for subsequent RUN commands
WORKDIR /some/path

# Declare paths to include in the cached layer snapshot
# (auto-detected for FETCH destinations and pip/uv installs)
SNAPSHOT /additional/path/to/capture

# Ignored (Dockerfile compat, no-op here):
# FROM, EXPOSE, CMD, ENTRYPOINT, LABEL, ARG, VOLUME, USER, SHELL

Usage

Single layer — build / restore

from scripts.containerfile import ContainerLayer

layer = ContainerLayer(
    containerfile_path="/path/to/Containerfile",
    cache_repo="oaustegard/claude-container-layers",  # GitHub repo for release assets
    gh_token="...",
)

# Try cache first, fall back to full build
layer.restore_or_build()

Or via CLI:

python -m scripts.cli restore /path/to/Containerfile --repo user/cache-repo

Multi-layer composition (v0.2.0+)

Decompose a heavy environment into named layers, each cached independently. Compose them in order on session start so most-changed bits don't invalidate stable bits.

from scripts.containerfile import compose

compose(
    containerfile_paths=[
        "layers/Containerfile",            # name='base'  (always-on)
        "layers/Containerfile.scientific", # name='scientific'
        "layers/Containerfile.mojo",       # name='mojo'
    ],
    cache_repo="user/cache-repo",
)

Each layer gets its own cache release tag layer-<name>-<hash> so retention policies (keep last N) and cache invalidation operate per-name.

Default layer names are derived from the Containerfile path:

• Containerfile → base
• Containerfile.scientific → scientific
• layers/Containerfile.X → X

CLI equivalent:

python -m scripts.cli compose \
    layers/Containerfile \
    layers/Containerfile.scientific \
    layers/Containerfile.mojo \
    --repo user/cache-repo

Per-layer name override

If filename doesn't derive cleanly, pass --name NAME:PATH per layer:

python -m scripts.cli compose \
    --name base:weird-named-file.txt \
    --name mojo:other-file.txt \
    weird-named-file.txt other-file.txt

Single-layer naming (back-compat)

build / restore / hash / inspect accept --name:

python -m scripts.cli restore Containerfile.mojo --name mojo
# Cache tag becomes 'layer-mojo-<hash>' instead of 'layer-<hash>'.
# Omit --name to keep the old back-compat tag for existing callers.

The uv Shim

After building, install the shim to capture future installs:

source /path/to/container-layer/scripts/uv_shim.sh /path/to/Containerfile

Now uv pip install foo both installs the package AND appends RUN uv pip install foo to your Containerfile.

Rebuilding the Cache

After modifying the Containerfile:

layer.build_and_push()  # Execute, snapshot, upload

Architecture

Read scripts/containerfile.py for the parser/executor and scripts/layer_cache.py for the GitHub Releases caching logic. The cache key is a SHA-256 of the Containerfile contents — any change triggers a rebuild.

Configuration

The skill expects these environment variables (or pass as constructor args):

• GH_TOKEN — GitHub token with repo scope (for releases)
• Cache repo can be any repo the token has write access to

Workflow Integration

This skill is designed to be invoked from a boot script. Example Containerfile:

# Skills
FETCH github:oaustegard/claude-skills /mnt/skills/user

# Python environment
RUN uv pip install --system pandas numpy requests

# Path config
RUN echo '/mnt/skills/user/remembering' > /usr/local/lib/python3.12/dist-packages/muninn-remembering.pth

# Custom setup
ENV MY_VAR=hello
WORKDIR /home/claude