intellectronica

allowed-tools	description	argument-hint	model
Bash(/opt/homebrew/bin/codex *)	Run codex with GPT-5-high	your prompt text	haiku

Run the following command exactly. Don't modify it in any way.

jmeiracorbal

# unistall orbstack first

# if you install OrbStack, this app uses the same commands and replaces docker symlinks, 
# if you uninstall it, the docker commands doesn't work. You need to restore the symlinks.

# remove old links orbstack
sudo rm -f /usr/local/bin/docker /usr/local/bin/docker-compose /usr/local/bin/docker-buildx /usr/local/bin/docker-credential-desktop

# create new links to docker desktop
sudo ln -s /Applications/Docker.app/Contents/Resources/bin/docker /usr/local/bin/docker

hrishioa

#!/usr/bin/env bun

import { existsSync, mkdirSync, writeFileSync } from "fs";
import { resolve } from "path";

// Define types based on the JSON structure
type Citation = {
  url: string;
  uuid: string;
  title: string;

ma7dev

# Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

**Operational success hinges on meticulous planning, precise execution, and self-validation of every task. Adherence to instructions and avoiding extraneous changes are paramount.**

## Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format, located within the `.cursor/rules/memory_bank/` directory. Files build upon each other in a clear hierarchy:

renschni

I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:

TLDR: Manus AI Agent Report

Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox

aashari

The Autonomous Agent Prompting Framework

This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.

The philosophy is simple: Autonomy through discipline. Trust through verification.

This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.

I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58

czue

diff --git a/assets/javascript/site.js b/assets/javascript/site.js
index a34accb4..32ded7dc 100644
--- a/assets/javascript/site.js
+++ b/assets/javascript/site.js
@@ -3,3 +3,5 @@
 import 'htmx.org';
 import './htmx';
 import './alpine';
+import 'flowbite';
+import 'flowbite/dist/datepicker';

alexweberk

hwchase17

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
import requests
from bs4 import BeautifulSoup
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.utilities import DuckDuckGoSearchAPIWrapper
import json

RESULTS_PER_QUESTION = 3

hwchase17

import pandas as pd
from pandasai import PandasAI

# Sample DataFrame
df = pd.DataFrame({
    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
    "gdp": [19294482071552, 2891615567872, 2411255037952, 3435817336832, 1745433788416, 1181205135360, 1607402389504, 1490967855104, 4380756541440, 14631844184064],
    "happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12]
})