autoresearch-coach-evolution

Mechanically optimize coaching microcopy by scoring individual texts using grep and wc metrics for concreteness, brevity, and actionability, then generating up to three constrained variants and accepting only those meeting a strict +5 mechanical improvement threshold.

Batch optimize localization ARB files by running automated sessions that score existing strings, generate up to three variants per target, enforce a +5 mechanical improvement threshold, and verify changes using flutter gen-l10n.

Yes, you can use grep and wc metrics to mechanically score microcopy clarity and actionability by evaluating concrete language, jargon, brevity, emotion, and voice to create an objective baseline for iterative text improvements.

The best way to enforce banned-term compliance when rewriting coaching strings is through automated mechanical scoring that checks iterative variants against strict grep/wc metrics and rejects any text containing prohibited terms before acceptance.

Run an automated 20-attempt session to improve Dart source service templates by executing a command that scores existing fallback messages, generates up to three constrained variants, verifies a +5 mechanical improvement, and commits accepted edits.

Mechanical scoring provides an objective baseline for microcopy optimization by using grep and wc metrics to measure concreteness and actionability, eliminating the subjective bias and error-prone nature of manual review processes.