A small progressive-disclosure Skill that demonstrates the SKILL.md plus Python-sidecar pattern with dependency-free descriptive statistics. The sidecar is loaded only when the Skill is selected, and it works on numeric sequences the caller passes in.
A Skill is a directory of files, so installing one is copying that directory
somewhere an agent looks. With Node 18+ and git on PATH (npm resolves a
github: spec through it), and nothing cloned:
npx github:PKU-YuanGroup/OpenAI4S install example_stats --target claude
--target claude writes to ~/.claude/skills, claude-project to
./.claude/skills, openai4s to <data_dir>/user-skills, and --dir <path>
to anywhere you name. The resolved absolute path is printed before anything is
written there, and --dry-run stops at that plan. A reinstall refuses to
overwrite a copy you have edited or one it did not install, and uninstall
removes only the files it wrote. For a recipe included in the npm release,
npx @pku-yuangroup/openai4s-skills install example_stats --target claude
installs the published copy. The npm catalog can differ from this repository.
Without Node, take the directory itself — and turn it into a .zip if an upload
field wants one. The tarball is the whole repository (over 100 MB), and the pipe
is a POSIX shell recipe (macOS, Linux, WSL) that extracts only this directory:
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \
| tar -xz --strip-components=2 OpenAI4S-main/skills/example_stats
python3 -m zipfile -c example_stats.zip example_stats
The click-through form of that same download is the
repository zip,
which is also the route to take on Windows: extract it and copy
skills/example_stats/ out
(unzip main.zip 'OpenAI4S-main/skills/example_stats/*' unpacks only that
directory). If you already run OpenAI4S there is nothing to install — the wheel
ships every bundled Skill, and a bundled Skill takes precedence over a
same-named copy in <data_dir>/user-skills. Targets, provenance, and what the
installer refuses to do:
tools/skills-installer/.
| File | Responsibility |
|---|---|
SKILL.md |
Import examples and short recipes for summaries, quantiles, z-scores, and Pearson correlation. |
kernel.py |
Optional sidecar over plain Python number lists: mean, sample or population std, median, an interpolated quantile, zscore, correlation, and a combined summary. Each one checks its input and refuses an empty sequence. |
These are ordinary calculations, meant for teaching and general use. They do not choose a statistical design, and they do not make an inference valid.