Choose Covariance Estimator

by microprediction

Choose Covariance Estimator is an AI Engineering skill for Claude Code, published by microprediction in precise.

333 stars58 forkson microprediction/preciseAdded 2026/08/12Repository updated 2026/06/24

View source on GitHub Download repo ZIP

covariance-estimationcovariance-matrixinvestment-portfolioportfolio

Install in seconds

Install Choose Covariance Estimator

Copy Choose Covariance Estimator into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/microprediction/precise/tree/main/.claude/skills/choose-covariance-estimator ~/.claude/skills/choose-covariance-estimator

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/microprediction/precise.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: .claude/skills/choose-covariance-estimator/SKILL.md in microprediction/precise
Installs to: ~/.claude/skills/choose-covariance-estimator
Collection: One of 6 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What Choose Covariance Estimator does

Choose Covariance Estimator picks a covariance estimator for a 2-D dataset using sample features like dimension, conditioning, and tail behavior. Use it when you need a shortlist of estimators or a safe default for ill-conditioned or heavy-tailed data.

Choose Covariance Estimator is cataloged under AI Engineering on DirSkills. Choose Covariance Estimator comes from a repository tagged covariance-estimation, covariance-matrix, investment-portfolio and portfolio.

Documentation

README

Excerpt

Choose a covariance estimator for your data

No estimator wins everywhere, so precise recommends one from observable, truth-free features.

from precise import suggest, covariance_features

suggest(X, top=3)        # -> list of estimator CLASSES, best first
covariance_features(X)   # -> dict of the features behind the choice

X is 2-D (rows = observations, columns = variables). Then:

Est = suggest(X, top=1)[0]
est = Est()
est.fit(X)               # or stream rows with partial_fit
cov = est.covariance_

What it keys on

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Choose Covariance Estimator

What else does microprediction publish alongside Choose Covariance Estimator?
Choose Covariance Estimator is one of 6 skills that DirSkills catalogs from microprediction/precise, the repository it ships in. Its siblings there include Covariance Assessment, Covariance Scoring and Estimate Online Covariance. Each one is a separate skill with its own page in this directory, installs the same way Choose Covariance Estimator does, and is maintained by microprediction in that same repository. The rest of the collection is listed on the microprediction/precise page.
How does Choose Covariance Estimator compare to other AI Engineering skills?
Choose Covariance Estimator ranks #2209 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Choose Covariance Estimator against them. Open each page to compare what they document and how they install.