gaasher

Agent Loop Skills

Name: Agent Loop Skills
Author: gaasher

Data community

SOURCE https://github.com/gaasher/Agent-Loop-Skills

Description

Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

agent-loop-skills

Loop until it's better — drop-in agentic loops, packaged as open-standard Agent Skills.

Autoresearch · scientific writing · data analysis · code/SQL/prompt optimization · red/blue/purple-teaming — each a *generic, reusable loop* you bind to **your own task at invocation time**, that iterates against a **real signal** until the work is actually better.

[![Agent Skills: open standard](https://img.shields.io/badge/Agent%20Skills-open%20standard-5b50e5)](https://agentskills.io/specification) [![Works in Claude Code](https://img.shields.io/badge/Claude%20Code-verified-2f7d4f)](#compatibility) ![status: experimental](https://img.shields.io/badge/status-experimental-orange) [![PRs welcome](https://img.shields.io/badge/PRs-welcome-brightgreen)](CONTRIBUTING.md) [![License: MIT](https://img.shields.io/github/license/gaasher/agent-loop-skills)](LICENSE) [![Stars](https://img.shields.io/github/stars/gaasher/agent-loop-skills?style=social)](https://github.com/gaasher/agent-loop-skills)

tournament-autoresearch improving a CIFAR-10 model from 0.734 to 0.798 val_acc over 11 iterations

A real run. The tournament-autoresearch loop on a CIFAR-10 model under a fixed 5-epoch budget — competing agents propose a change each step, a self-calibrating judge keeps the winners (green) and discards the regressions (gray): 0.734 → 0.798 val_acc, hands-off, 7 of 11 kept. Full ledger: showcase/tournament-autoresearch.
Far from SOTA by design — a deliberately tiny CNN at 5 epochs on a laptop GPU (Apple MPS). The demo is the loop's decision-making, not the absolute accuracy.