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by austin-starks · Agent Tool · ★ 211
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Promptimizer – Automated AI-Powered Prompt Optimization Framework This project implements an automated system for optimizing AI prompts using genetic algorithms and machine learning techniques. It's designed to evolve and improve any LLM prompt. The example in this repo is focused on AI-driven stock screening. Read more about it here. Warning/Disclaimer Optimizing prompts using this framework can be VERY expensive! I accept no liability for any costs incurred. If cost is a consideration, please use local LLMs like Ollama for optimization. Check out NexusTrade To see the results of the optimized prompt, check out NexusTrade.io. NexusTrade is an AI-Powered automated trading and investment platform that allows users to create, test, optimize, and deploy algorithmic trading strategies.
| Stars | 211 |
| Forks | 22 |
| Language | TypeScript |
| Category | Agent Tool |
| Quality Score | 67.9707029739906/100 |
| Open Issues | 1 |
| Last Updated | 2024-07-31 |
| Created | 2024-07-26 |
| Platforms | node |
| Est. Tokens | ~18k |
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Promptimizer is An Automated AI-Powered Prompt Optimization Framework. It is categorized as a Agent Tool with 211 GitHub stars.
Promptimizer is primarily written in TypeScript. It covers topics such as ai, anthropic, aritificial-intelligence.
You can find installation instructions and usage details in the Promptimizer GitHub repository at github.com/austin-starks/Promptimizer. The project has 211 stars and 22 forks, indicating an active community.
The top alternatives to Promptimizer on Agent Skills Hub include cai, samples, agent-builder. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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