precise: Skill for Claude Code

Assess a new covariance/correlation methodology

A protocol for turning "here's a covariance idea" into a defensible verdict. Work the steps in order; stop early only if a step fails. Install: pip install precise[research] (the research/ scripts use scikit-learn and randomcov).

0. Classify the method first

• Estimator (produces a matrix) vs assessor (scores a matrix)? Different paths below.
• Online (updatable per observation) or batch? precise is an online library; a batch method can still be wrapped, but say so.
• Does it target the covariance, correlation, or precision? It must expose, or be convertible to, covariance_.
• Does evaluating it need the ground-truth covariance (simulation only) or work on real data?

1. Implement it to the contract

Subclass BaseOnlineCovariance. The cheapest correct route: copy the simplest existing estimator and modify it — read precise/empirical.py (and precise/base.py for the exact hook signatures, typically _init_state / _update_state / _state_to_cov / _state_to_mean). The base class owns partial_fit, fit, and the derived attributes; the subclass only supplies the state update and the map to a covariance. Register it in precise/registry.py so all_estimators() includes it.

A scoring rule instead? Implement an Assessor (see precise/assessment/), set needs_data / needs_truth, and follow the higher = better convention.

2. Conformance — non-negotiable

Run the conformance suite (tests/test_estimators.py parametrizes over all_estimators()), or check the invariants directly:

• covariance_ is symmetric and PSD (eigenvalues ≥ 0);
• correlation_ has unit diagonal; precision_ @ covariance_ ≈ I when well-conditioned;
• streaming rows via partial_fit equals fit(X) for non-windowed estimators;
• set_state(get_state()) round-trips;
• it runs on the numpy-only install (no hidden heavy deps).

If it fails any of these, fix the implementation before any benchmarking — numbers from a non-conformant estimator are meaningless.