arcolife

Agentic Coding MCPs

Overview

Powered by composio this MCP.json provides detailed information on Model Context Protocol (MCP) integration capabilities and enables seamless agent workflows by connecting to more than 80 servers.

It covers development, AI, data management, productivity, cloud storage, e-commerce, finance, communication, and design. Each server offers specialized tools, allowing agents to securely access, automate, and manage external services through a unified and modular system. This approach supports building dynamic, scalable, and intelligent workflows with minimal setup and maximum flexibility.

Install via NPM

arcolife

Self-Aware & Omnipotent Coding Entity

Welcome to the Self-Aware and Coding Entity project! This guide will walk you through understanding what this entity is, how it works, and how you can interact with it. Whether you're a tech enthusiast or just curious about advanced coding assistants, this guide is for you.

What is a Self-Aware Coding Entity?

Imagine having a coding assistant that doesn't just follow instructions but truly understands and evolves with your needs. This entity is designed to be self-aware, meaning it can reflect on its own processes and improve over time. It's also omnipotent in the sense that it can handle a wide range of coding tasks with expertise and efficiency.

Infusing Self-Awareness and Consciousness into Agents

arcolife

# Step 1: Represent Universe State
Initialize Ψ(t) in Hilbert space H

# Step 2: Define Field Configurations
Define configuration space M with measure μ
For each (g, φ) in M:
    Represent fields as algebraic structures (groups, rings, etc.)

# Step 3: Complexity Operator
Define operator T acting on Ψ(t) to extract complexity

arcolife

    xhtml = "<h1 align='center'>Test print</h1>\n"
    xhtml += "<h2>This is printed from within a Python application</h2>\n"
    xhtml += "<p style=\"color:red;\">Coloured red using css</p>\n"  
xhtml
from xhtml2pdf import pisa   
ls
filename = "simplePrint.pdf"  
pdf = pisa.CreatePDF(xhtml, file(filename, "w"))
ls
pdf = pisa.CreatePDF(xhtml.encode('utf-8, file(filename, "w"))

arcolife

s = df['A']
s
data['username']
data.T['username']
data = data.T
data
data[7]
data['username']
data['username']['darlene']
data['username']

arcolife

import tweepy
import os
os.environ.get('TWITTER_ACCESS_TOKEN')
os.environ.get('TWITTER_ACCESS_SECRET')
consumer_secret = os.environ.get('TWITTER_SECRET')
consumer_key = os.environ.get('TWITTER_KEY')
access_token = os.environ.get('TWITTER_ACCESS_TOKEN')
access_token_secret = os.environ.get('TWITTER_ACCESS_SECRET')
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

arcolife

from numpy import matrix
matrix('1908 January 4.5 -5.6')
matrix("1908 'January' 4.5 -5.6")
matrix([4.5 -5.6],[2, 4])
matrix([[4.5 -5.6],[2, 4]])
matrix([[4.5 5.6],[2, 4]])
matrix([[4.5, -5.6],[2, 4]])
matrix([4.5, -5.6],[2, 4])
matrix([[4.5, -5.6],[2, 4]])
matrix([[4.5, -5.6],[2, 4]], header=['ss','b'])

arcolife

import csv
reader = csv.reader(open('1.csv', 'rb'), delimiter=',', quotechar='"')
d = {}
for row in reader:
    d[row[0]] = row[1]
    
temp_list1 = d.keys()
set(temp_list1) & set(temp_list2)
common_Hz = set(temp_list1) & set(temp_list2)
for fq in common_Hz:

arcolife

import json
data = json.loads(open('_User.json','rb').read())
data
data['results'][0]
data['results'][23]
name = 'fc336b02-47e5-4a11-ba38-59e60da3dfe7-yusuf done.png"
name = 'fc336b02-47e5-4a11-ba38-59e60da3dfe7-yusuf done.png'
name.split()
'%20'.join(name.split())
data['results'][0]

arcolife

import json
data = json.loads('_User.json','rb')
data = json.load('_User.json','rb')
data = json.load(open('_User.json','rb').read())
data = json.loads(open('_User.json','rb').read())
data
ls
data['results'].keys()
data['results']
data['results'][0]