qm-heisenberg-picture
Domain: qm · Model class: strong
Description
Section titled “Description”Use this skill when you need to analyse how code quality metrics drift over time and find which metrics are compatible vs competing. Trigger phrases include: “how are my metrics changing over time”, “Heisenberg picture of code metrics”, “which metrics are commuting”, “metric drift analysis”, “find anti-correlated metrics”, “which quality indicators conflict with each other”. This skill computes metric drift rates and pairwise Pearson correlations to classify metric pairs as COMMUTING or NON_COMMUTING. Do NOT use when fewer than 3 metric snapshots are available.
Purpose
Section titled “Purpose”Analyse how code quality metrics (operators) evolve across a series of snapshots while treating the codebase state as fixed. This mirrors the Heisenberg picture in quantum mechanics, where operators carry all time-dependence while the state vector remains constant. The tool computes per-metric drift rates and pairwise Pearson correlations to classify metric pairs as COMMUTING (compatible — measuring one does not disturb the other) or NON_COMMUTING (competing — improving one degrades the other, e.g. complexity vs. test coverage).
Trigger Phrases
Section titled “Trigger Phrases”- “qm-heisenberg-picture”
Anti-Triggers
Section titled “Anti-Triggers”None defined.
Intake Questions
Section titled “Intake Questions”- Apply the qm-heisenberg-picture skill to the user request.
Output Contract
Section titled “Output Contract”- physics metaphor output
- plain-language engineering translation
- confidence and limitation notes
- recommended engineering action
Related Skills
Section titled “Related Skills”None