What problem does it solve? Single-pass improvement cycles lose context between runs: dead ends get re-explored, rejected approaches resurface, and there is no way to tell whether quality actually improved. This Skill carries ISC criteria, learnings, and a dead-ends ledger across repeated refinement rounds so each pass starts from the last one's endpoint. ## Core Features & Use Cases - Multi-round refinement: Runs complete OBSERVE-to-LEARN DevOS Loop cycles against a target file, directory, or skill, with each round inheriting the previous round's context. - Human-in-the-loop by default: A person reviews and steers between rounds, approving or redirecting the next cycle. - Autoresearch mode: An opt-in --autoresearch flag enables autonomous iteration with a dead-ends ledger and MAD-based confidence gating on iteration scores. - Use Case: Point it at a prompt file with a goal like "more concise, less filler" and run 5 iterations, reviewing the output after each round before continuing. ## Quick Start Ask the agent to loop over a target file with a stated goal and an iteration count, for example: run /loop on prompts/summarize.md with the goal of making it more concise for 3 iterations.