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Agentic Loop Engineering Course

Development community

Description

An 18 notebook course that isolates and measures each component of agentic loop engineering on real, industry standard software datasets.

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

Agentic Loop Engineering

Executed and Measured

A hands-on, fully executed course that isolates **every component of loop engineering** and proves its contribution with real, reproducible numbers on hard, industry standard software datasets.

![python](https://img.shields.io/badge/python-3.10%2B-3776AB?logo=python&logoColor=white) ![jupyter](https://img.shields.io/badge/Jupyter-18%20notebooks-F37626?logo=jupyter&logoColor=white) ![served by vLLM](https://img.shields.io/badge/served%20by-vLLM-111111) ![model](https://img.shields.io/badge/model-Qwen2.5--Coder--32B--AWQ-FF6F00) ![license](https://img.shields.io/badge/license-MIT-3DA639) ![results](https://img.shields.io/badge/results-reproducible-2ea44f)


The leverage point has moved from prompts, to context, to harness, to loops

**The one idea behind all 18 notebooks:** a loop is only as good as the verifiable signal it is wired to. A loop that re-runs an agent against its own opinion barely improves. A loop wired to an executable check (a test, a schema, a retrieved fact, a real evaluation harness) measurably does. Everything below is the evidence, produced on a single A100 80GB GPU with a self hosted open model, captured inline, and traceable to a JSON file in `results/`.


Contents