tune-system

You can automate maintenance tuning by loading metrics from evolution-state.yaml, changelog.md, and workflow data to generate evidence-based recommendations for cadence and threshold adjustments. This process requires minimal human intervention while providing data-driven change proposals.

Conservative system tuning is the practice of making small, evidence-based adjustments to automation cadence and thresholds. It analyzes operational metrics to provide traceable rationale and risk-aware impact assessments, ensuring changes keep processes aligned with goals without disruptive overcorrections.

You should run maintenance tuning during monthly maintenance cycles, milestone reviews, or after notable system failures. These triggers ensure the automation system receives timely analysis and conservative adjustments based on current operational data and workflow metrics.

To generate an auditable record of threshold changes, run a tuning analysis that produces a structured report and automatically updates the changelog. This creates traceable documentation of adjustments made to evolution-state.yaml and related task settings with risk-aware impact assessments.

Yes, the analysis requires evolution-state.yaml, changelog.md, and related obsidian workflow metrics to function. These files provide the operational data needed to identify conservative tuning opportunities and generate data-driven change recommendations with traceable rationale.

Automated tuning for maintenance cadence focuses on conservative adjustments rather than large-scale process overhauls. It provides recommendations with risk-aware impact assessments, meaning users still need to review and manually apply proposed changes to evolution-state.yaml and related task settings.