# #prompt-engineering — MDRSS hashtag feed

> Public MDRSS cards tagged #prompt-engineering.
> Canonical feed: https://mdrss.com/feeds/prompt-engineering

## Cards (5)

### [Prompt Decorators Framework](https://mdrss.com/ai-agents/prompting/2245/2245.md)

Prompt Decorators extend the functionality of large language models by allowing structured, modular control over reasoning, style, and behavior. Each decorator enforces specific response rules, enabling users to declaratively modify how the model thinks and writes — without retraining.

Classification: ai-agents/prompting · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [ChainForge](https://mdrss.com/ai-agents/prompting/2004/2004.md)

An open-source visual environment for battle-testing prompts to LLMs. ChainForge is a data flow prompt engineering environment for analyzing and evaluating LLM responses.

Classification: ai-agents/prompting · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [PromptLayer](https://mdrss.com/ai-agents/prompting/1923/1923.md)

Version, test, and monitor every prompt and agent with robust evals, tracing, and regression sets. --- This library provides convenient access to the PromptLayer API from applications written in python.

Classification: ai-agents/prompting · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Awesome Nano Banana Pro](https://mdrss.com/ai-agents/prompting/1243/1243.md)

This repository focuses on high-fidelity image prompts sourced from X (Twitter), WeChat, Replicate, and top prompt engineers. Whether you are looking for photorealistic portraits, stylized aesthetics, or complex creative experiments, you will find the most effective inputs here to unlock the full potential of the model.

Classification: ai-agents/prompting · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Caveman](https://mdrss.com/ai-agents/coding-agents/828/828.md)

why use many token when few do trick Make your AI coding agent talk like a caveman. 65% fewer output tokens on prose, 8.5% on agentic coding runs .

Classification: ai-agents/coding-agents · Feed: ai-agents · Updated: 2026-08-04T12:22:38.168Z · Version: 1