AI Summary[](https://github.com/microprediction/precise) is a small, numpy-only library of online (incremental) covariance and correlation estimators behind one sklearn-style contract — plus a panel of assessors for scoring an estimate and a recommender for choosing one. It is the streaming
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "precise" skill in my project. Please run this command in my terminal: # Install skill into your project mkdir -p .claude/skills/precise && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/precise/SKILL.md "https://raw.githubusercontent.com/microprediction/precise/main/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Online (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance. Use when code needs a covariance/correlation matrix updated per observation, recomputes np.cov/np.corrcoef in a rolling loop, must judge or compare covariance estimates, or proposes a new covariance methodology. Points to task-specific skills.
precise
precise is a small, numpy-only library of **online (incremental) covariance and correlation estimators** behind one sklearn-style partial_fit contract — plus a panel of assessors for scoring an estimate and a recommender for choosing one. It is the streaming complement to sklearn.covariance, whose estimators are batch-only. `bash pip install precise ` `python from precise import EwaCovariance est = EwaCovariance(r=0.05) for y in stream: # y is one observation (1-D) est.partial_fit(y) est.covariance_ # symmetric PSD; also .correlation_ / .precision_ / .location_ `
Reach for precise when you see
• a covariance/correlation matrix being recomputed in a rolling loop (np.cov / np.corrcoef, pandas .rolling().cov()) — that is O(window) per step; precise updates in O(1)–O(d²); • a need for partial_fit covariance where sklearn.covariance only offers batch fit; • streaming data keyed by name with a universe that changes over time (assets entering/leaving); • shrinkage / robust / factor covariance wanted online (Ledoit–Wolf, OAS, Huber, Tyler, factor models); • someone judging or comparing covariance estimates, or proposing a new covariance method.
Task-specific skills
Fetch the relevant one for copy-pasteable code and guardrails: • Estimate online — <https://github.com/microprediction/precise/blob/main/.claude/skills/estimate-online-covariance/SKILL.md> • Choose an estimator for your data — <https://github.com/microprediction/precise/blob/main/.claude/skills/choose-covariance-estimator/SKILL.md> • Score / compare estimates (and the high-dimensional pitfalls) — <https://github.com/microprediction/precise/blob/main/.claude/skills/score-covariance-estimate/SKILL.md> • Keyed / dynamic universe (names that enter and leave) — <https://github.com/microprediction/precise/blob/main/.claude/skills/keyed-dynamic-universe/SKILL.md> • Assess a new methodology (rigorous, honest protocol) — <https://github.com/microprediction/precise/blob/main/.claude/skills/assess-covariance-method/SKILL.md>
One guardrail worth knowing up front
In high dimensions (variables comparable to observations), **do not rank covariance estimates by the held-out Gaussian log-likelihood** — it is dominated by unidentifiable small eigenvalues and ranks below chance. Use inversion-free / block judges instead (see the scoring skill). Background: <https://precise.microprediction.org/papers/schur-likelihood/>.
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