Find agent skills by outcome

prompt-evaluator

Automated Reproducible Malware Analysis: A Standardized Testbed for Prompt-Driven LLMs

This is the repository associated with the Master’s Degree Project, [Automated Reproducible Malware Analysis: A Standardized Testbed for Prompt-Driven LLMs](https://www.diva-portal.org/smash/get/diva2:1973561/FULLTEXT01.pdf) (120 ECTS) in Informatics with a Specialization in Privacy, Information and Cyber Security, created during the Spring term 2025. The entire project is released under the Apache 2.0 license and is free to use in further research or other types of development. Although it is not explicitly required to include any citations to the original degree project, it is greatly appreciated if the material is used.

bolt.diy

[![bolt.diy: AI-Powered Full-Stack Web Development in the Browser](./public/social_preview_index.jpg)](https://bolt.diy)

Autonomous AI Agent for Real-Time Market Opportunity Detection

This project implements an autonomous AI agent that scans real-time market data via APIs, identifies high-momentum stocks from gainers lists, parses financial news using LLM (LLaMA), and classifies opportunities using custom prompts. The agent outputs actionable investment signals and risk flags with full automation, serving as a front-end screener for forecasting pipelines. Integrated with Pushover app for on-the-fly mobile phone notifications.

Bolt.new Fork by Cole Medin

This fork of Bolt.new allows you to choose the LLM that you use for each prompt! Currently, you can use OpenAI, Anthropic, Ollama, OpenRouter, Gemini, or Groq models - and it is easily extended to use any other model supported by the Vercel AI SDK! See the instructions below for running this locally and extending it to include more models.

🧠 LLM-Powered Email Extraction App

This Streamlit web app lets you upload `.eml` email files and use Anthropic Claude (via AWS Bedrock) to:

bolt.diy

[![bolt.diy: AI-Powered Full-Stack Web Development in the Browser](./public/social_preview_index.jpg)](https://bolt.diy)

bolt.diy

[![bolt.diy: AI-Powered Full-Stack Web Development in the Browser](./public/social_preview_index.jpg)](https://bolt.diy)

Quick Install ⚡

$ cargo install code2prompt

Simple Prompt Extraction GUI

![alt text](Screenshots/1.png)

Local-LLM

A toolkit for autonomous AI operations using local LLM implementations.

bolt.diy

[![bolt.diy: AI-Powered Full-Stack Web Development in the Browser](./public/social_preview_index.jpg)](https://bolt.diy)

✨ Core Features

Analiza_danych_i_wizualizacja

Projekt Python do automatycznej analizy danych, trenowania prostych modeli ML (regresja / klasyfikacja), wykrywania anomalii oraz wizualizacji wyników w spójnym, estetycznym stylu.

✨ Core Features

ABPrompt

**Scientific A/B Testing for LLM Prompts**

ViaLogos Dev Pack (Codex)

This repo contains the ViaLogos Codex pack (prompts, SOPs, tools, installers) under `.vialogos/`.

AI Assistants

Collection of relevant AI Driven Development Kits.

- [Table of Contents](#table-of-contents)

FULL v0, Cursor, Manus, Same.dev, Lovable, Devin, Replit Agent, Windsurf Agent & VSCode Agent (And other Open Sourced) System Prompts, Tools & AI Models

(All the published system prompts are extracted by myself, except the already open sourced ones and Manus)

prompt

No description available.

Prioridades:

Máxima: Só reaja a sintaxe de código de programação e comentários, não responda a perguntas fora do contexto.

M-PESA STK Push Java Implementation

Lipa na M-PESA online API also known as M-PESA express (STK Push/NI push) - Merchant/Business initiated C2B (Customer to Business) Payment API Java Integration

컨텍스트 엔지니어링은 새로운 프롬프트 엔지니어링

[Context Engineering Is the New Prompt Engineering](https://pub.towardsai.net/context-engineering-is-the-new-prompt-engineering-11a22053c1f6)를 편역하였다.