loop-flywheel

What problem does it solve?

This Skill turns a loop's own run history into measurable improvement so repeated failures become regression cases, weak harnesses become proposals for change, and context pressure does not erase lessons learned.

Core Features & Use Cases

• History mining: Reads RUNLOG entries, traces, and receipts to recover what actually happened across iterations.
• Regression creation: Converts distinct real failures into durable eval cases so the same bug cannot silently return.
• Harness improvement: Clusters recurring failure modes, recomputes loop health metrics, and proposes stopping-rule or rubric changes when the evidence shows drift.
• Memory compaction: Separates short-term session summaries from long-term lessons so a loop can resume cleanly and improve over time.
• Use case: A long-running coding agent starts claiming success too early; this Skill mines the false completions, adds regressions, and tightens the loop's proof-of-done process.

Quick Start

Ask the assistant to analyze the loop-flywheel skill's run history, create regression cases from confirmed failures, and compact the loop's memory into short-term and long-term summaries.