Agent-Loop-Skills

This Skill addresses the issue where AI agents tend to "spin wheels" or produce inconsistent output quality in complex tasks. In actual development or research, developers often require AI to perform multi‑round iterative optimization—for example, tuning code to improve model accuracy, optimizing SQL query performance, or polishing academic papers. A single prompt is usually insufficient to achieve the desired outcome, and there is often a lack of objective feedback mechanisms to verify whether each iteration actually produces an improvement.

Usage is straightforward: you simply select a pre‑built generic loop skill (such as tournament‑autoresearch) and bind it to your specific task objective when invoking it (e.g., “optimize the accuracy of this CIFAR‑10 model”). The loop will then automatically execute the following flow: “propose a modification → execute and verify it in your environment → evaluate based on real‑world signals (e.g., test scores, performance metrics) → retain effective improvements → log results and proceed to the next round,” continuing until the preset termination condition is met.

It is particularly suitable for teams or individuals who need systematic, automated exploration and optimization. This includes developers, researchers, or data analysts who are already familiar with a specific domain (such as machine learning, data analysis, or code optimization) but wish to offload repetitive trial‑and‑error and validation processes to an AI agent. For scenarios that require continuous improvement of work outputs based on objective metrics (rather than subjective judgment), this skill library provides a standardized solution.

We recommend using this tool uniformly for tasks that have clear, quantifiable evaluation criteria (e.g., unit‑test pass rate, model validation‑set accuracy, query latency). Note that the loop primarily relies on the “real‑world signals” you provide to drive iteration, so it is crucial to ensure your validation environment can stably and automatically supply such feedback. Also, because each loop is generic, first‑time users should carefully understand its workflow and configuration parameters.