agent-routing
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe…
Authors and caches a personalized container environment from a Dockerfile- like spec, as a single layer or as a composition of independently cached layers. Use when the user mentions "container layer", "Containerfile", "custom container", "cache my installs", "composable
$ npx -y skills add oaustegard/claude-skills --skill container-layer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/container-layerContext preview
The summary Claude sees to decide when to auto-load this skill.
Authors and caches a personalized container environment from a Dockerfile- like spec, as a single layer or as a composition of independently cached layers. Use when the user mentions "container layer", "Containerfile", "custom container", "cache my installs", "composable
name: container-layer description: >- Authors and caches a personalized container environment from a Dockerfile- like spec, as a single layer or as a composition of independently cached layers. Use when the user mentions "container layer", "Containerfile", "custom container", "cache my installs", "composable layers", "uv shim", or wants package installations, skills and environment config to survive an ephemeral session. Also for snapshotting, restoring or rebuilding that environment, and for capturing ad-hoc installs into a reproducible spec. metadata: version: 0.4.0
Build a reproducible, cached environment overlay for ephemeral containers using a Dockerfile-like spec.
This authors and caches a layer spec. It is not a Docker troubleshooting tool.
| Situation | Use | |---|---| | A build is slow or failing | read the build log; this skill will not help | | Managing a running container | docker/podman directly | | Session boot sequence and hooks | the workspace's own boot docs |
The tell is tense: this skill is for the environment you want next session, not the container you are fighting now.
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
# 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
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
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:
CLI equivalent:
python -m scripts.cli compose \
layers/Containerfile \
layers/Containerfile.scientific \
layers/Containerfile.mojo \
--repo user/cache-repoIf 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`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.
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.
After modifying the Containerfile:
layer.build_and_push() # Execute, snapshot, upload
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.
The skill expects these environment variables (or pass as constructor args):
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
A collection of Claude skills distributed as a Claude Code plugin marketplace. Skills are primarily designed for the Claude.ai PaaS skill compute environment; mileage will vary if used as Claude Code Agent Skills.
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