Forem: Shivam

Rival Guru : Know realtime facts about your competitors.

Shivam — Mon, 26 May 2025 06:43:34 +0000

This is a submission for the Bright Data AI Web Access Hackathon

What I Built

Ever wondered what your competitors are up to right now? Rival Guru fetches real-time data about your website's competitors and presents it in a clean, easy-to-analyze table format. 📊

🔍 Key Features:

Live tracking of competitor websites
Instantly updated metrics like traffic, keywords, backlinks & more
Organized in a sortable, filterable table for fast comparison
Designed for marketers, SEO specialists, and digital strategists

Whether you're looking to stay ahead or find opportunities, this dashboard gives you the insights you need—when you need them.

Demo

Github Repo

shivamarora1 / competitor-analysis

Want to beat the competition? Start by understanding them.

This repository is a submission for the BrightData Realtime AI Agent Challenge.
You can read the full submission article here.

Rival Guru

Want to beat the competition? Start by understanding them. This tool helps you analyze competitors in your market space, track their features, and identify opportunities for differentiation.

Overview

This project provides a Streamlit-based web application for tracking and analyzing competitors. It allows you to:

Compare competitor features side-by-side
Identify market gaps and opportunities
Generate insights based on collected data

This project uses Bright data browser api , so make sure you have account in bright data and created API_TOKEN and BROWSER_AUTH

Setup Instructions

Prerequisites

Python 3.8 or higher
Git

Installation

Clone this repository

git clone <repository-url>
cd competitor-analysis

Create and activate a virtual environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```
Set up .env

API_TOKEN=
WEB_UNLOCKER_ZONE= 
BROWSER_AUTH=

…

View on GitHub

How I Used Bright Data's Infrastructure

I’ve been using Bright Data’s Browser API to extract valuable insights about competitor companies—and it’s been incredibly effective.

🔍 Here's how it works in my workflow:

I navigate to the target website whose competitors I want to analyze.
Using SimilarWeb, I gather detailed data on competitors, including traffic, engagement, and ranking.

The Browser API makes the entire process seamless by allowing interaction with the page just like a real user—no need to deal with complex selectors or HTML structures.

Performance Improvements

Working with the Bright Data MCP Browser API has significantly simplified my data extraction workflow. One standout feature? I no longer need to hunt for CSS selectors or HTML field names.

✨ Why it's a game-changer:

You can extract data directly using visible field names on the screen — no need to inspect the source code.
This means I can focus purely on the data I need, rather than the technical structure behind it.
The built-in captcha solving is incredibly efficient and saves tons of manual effort.

Thanks to this, my workflow is faster, cleaner, and more scalable — perfect for real-time competitor analysis and other dynamic use cases.

🧠 Highly recommend it for anyone looking to automate web intelligence without getting bogged down in front-end details.

Generate Git action CI/CD pipeline using Amazon Q CLI

Shivam — Sun, 11 May 2025 17:09:13 +0000

This is a submission for the Amazon Q Developer "Quack The Code" Challenge: Crushing the Command Line

GitHub Actions is a popular CI/CD platform used to build, test, and deploy your code directly from GitHub. It allows you to automate workflows such as code reviews, running test cases, building applications, and deploying them.

In this tutorial, we will learn how to use Amazon Q to create GitHub Actions workflows for your application.

Amazon Q CLI is a generative AI-powered software development assistant. It understands the context of your code and provides an interactive way to ask questions and make changes to your code. Amazon Q CLI includes commands like q chat and q translate, which let you ask questions about your codebase and convert natural language instructions into shell commands.

Let's see how you can interact with Amazon Q CLI to generate GitHub Actions workflows for a CI/CD pipeline using natural language. Amazon Q supports multi-turn conversations, so you can refine the GitHub Actions file or provide custom instructions through a dialogue-based interface.

This tutorial assumes that you have configured Amazon Q CLI on your local machine. If not, please follow below link.

Ricardo Sueiras for AWS

Apr 2 '25

Getting started with Kiro CLI

#genai #aws

Comments 1

8 min read

We will refer to this project in our tutorial.

shivamarora1 / sakai-react

Free React Admin Template

This repository is a submission for the Amazon Q Chanllenge.
You can read the full submission article here.

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

Learn More

To learn more about Next.js, take a look at the following resources:

Next.js Documentation - learn about Next.js features and API.
Learn Next.js - an interactive Next.js tutorial.

View on GitHub

Our first objective is to add a GitHub Actions CI workflow to the project. Open your terminal and paste below command.

q chat "Add Github action workflow to this project that will whenever code is pushed to any branch. This workflow is for CI. It checks the formatting, linting and checks whether project is building or not."

When you give this instruction, Amazon Q CLI will take some time to process. It will analyze the entire project to understand the structure. Then, it will prompt you to create a .github/workflows folder and a ci.yaml file with all the given instructions.

Let's push the code and test this workflow.

Cool! We successfully created our first workflow using the Amazon Q AI software assistant.

Next step is to write workflow for building and deploying our project. Since this is static website we can deploy our workflow to public S3 bucket.

q chat "Add github action workflow to deploy this project amazon s3 bucket"

Within few seconds it generated code for building and deploying application to S3 bucket.

Since *Amazon Q CLI * is multi turn conversational assistant , It has also generated instructions to create a bucket in Amazon with public access ACL policy. This is pretty helpful documentation for configuring deployment environment.

Unlike other AI assistants, it won't leave you midway—it will also guide you on the next steps to take. In our case, the next steps are to create AWS access keys and secrets, and configure them in the GitHub repository.

Code Repository

shivamarora1 / sakai-react

Free React Admin Template

This repository is a submission for the Amazon Q Chanllenge.
You can read the full submission article here.

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

Learn More

To learn more about Next.js, take a look at the following resources:

Next.js Documentation - learn about Next.js features and API.
Learn Next.js - an interactive Next.js tutorial.

View on GitHub

Conclusion

CLI based AI assistant is really cool for developers. As a developers we spent alot of times on CLI. We work with a lot of open source tools it become tough to remember commands to run them. In such cases CLI based AI assistant is really helpful.

How run LLM in local using Docker.

Shivam — Sun, 27 Apr 2025 18:29:40 +0000

Self-hosted LLMs are gaining a lot of momentum. They offer advantages such as improved performance, lower costs, and better data privacy. You don't need to rely on third-party APIs, which means no unexpected increases in latency, no sudden changes in model behavior, and more control over your LLM.

However, running a model locally is a task in itself. There is currently no standard way or tool available to run models on local machines.

Docker model runner:

Docker Model Runner makes running AI models as simple as running a container locally — just a single command, with no need for additional configuration or hassle.

If you are using Apple Silicon, you can take advantage of GPU acceleration for faster inference.

The Docker local LLM inference engine is built on top of llama.cpp. This engine is exposed through an OpenAI-compatible API. Before running an LLM model using Docker, make sure you are using Docker Desktop version 4.40 or later.

Running a model is similar to running a container. First, start by pulling a model.

docker model pull ai/llama3.1

Full list of available models are here.

Once the pull is complete, your model is ready to use. You don't need to manually run any containers — Docker will automatically use its inference API server endpoint to handle your requests.

You can access your model from other containers using the http://model-runner.docker.internal/engines/v1 endpoint:

curl http://model-runner.docker.internal/engines/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "ai/llama3.1",
        "messages": [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Please write 500 words about the fall of Rome."
            }
        ]
    }'

If you want to access the model from host processes (i.e., the machine where Docker is running), you need to enable TCP host access:

docker desktop enable model-runner --tcp 12434

Here, 12434 is the TCP port where your model will be accessible.
You can then make requests from the host just like this:

curl http://localhost:12434/engines/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "ai/llama3.1",
        "messages": [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Please write 500 words about the fall of Rome."
            }
        ]
    }'

With everything up and running, you're ready to make local LLM calls!
You can use this endpoint with any OpenAI-compatible clients or frameworks.

Deploy HUGO website to Amazon S3 using Pulumi.

Shivam — Mon, 07 Apr 2025 06:32:15 +0000

HUGO

HUGO is a acclaimed world's fastest static website builder. HUGO is a open source framework. It was launched in 2013 and at present it has more than 79,249 stars on github. Checkout HUGO official library:

gohugoio / hugo

The world’s fastest framework for building websites.

A fast and flexible static site generator built with love by bep, spf13, and friends in Go.

Overview

Hugo is a static site generator written in Go, optimized for speed and designed for flexibility. With its advanced templating system and fast asset pipelines, Hugo renders a complete site in seconds, often less.

Due to its flexible framework, multilingual support, and powerful taxonomy system, Hugo is widely used to create:

Corporate, government, nonprofit, education, news, event, and project sites
Documentation sites
Image portfolios
Landing pages
Business, professional, and personal blogs
Resumes and CVs

Use Hugo's embedded web server during development to instantly see changes to content, structure, behavior, and presentation. Then deploy the site to your host, or push changes to your Git provider for automated builds and deployment.

Hugo's fast asset pipelines include:

Image processing…

View on GitHub

We will not go in detail about how to use HUGO but focus more on how to deploy static website generated by HUGO framework using Pulumi.

Few words on Pulumi IAC

Pulumi IAC (Infrastructure as Code) engine is used to automate the provisioning and management of infrastructure. Too geeky? Let me make it simple. Before Pulumi, If you want to provision or manage any cloud resource then you need to go respective cloud provider dashboard (AWS, Azure) and interact to their UI to perform any action. This method fails in some way with repeatability and consistency issues.

Pulumi treats cloud resources (i.e. servers, networks and storage) as code, allowing you to follow modern approaches to embrace and support full software engineering life cycle. You can do version control, automation and auto configure cloud resources easily using Pulumi IAC.

Enough talk let's go to code.

First things first, We will quickly set up and generate static site using HUGO. For more in depth detail you can follow official website of HUGO.

1.Install HUGO in your local machine.

brew install hugo

2.Set up repo in your local. To save I have already pushed website code in github repo.

git clone https://github.com/shivamarora1/hugoXPulumi

3.Generate static website.

hugo

4.If everything works as expected then you should be able to see your website on

http://localhost:1313/

Built site is stored in public folder.

Great work. Now our next task is to deploy this website to AWS S3 so that general public can access our cool website. We are gonna use Pulumi for that.
S3 is a cloud storage service where you can store and retrieve data online from anywhere and anytime.

Setting up AWS security credentials

When you interact with AWS, you require your AWS security credentials so that AWS can authenticate and authorize access to cloud resource.
Following video gives you process to create AWS security credentials.

Once you have AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY , go to terminal and export them using below command:

export AWS_ACCESS_KEY_ID=<YOUR_AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<YOUR_AWS_SECRET_ACCESS_KEY>
export AWS_REGION=ap-south-1

Pulumi will use these credentials to interact with AWS dashboard.

Setting up Pulumi script

Before you get start make sure pulumi is installed on your system.

brew install pulumi/tap/pulumi

You can refer pulumi documentation for installation in other platforms.
We are going to use python runtime , so make sure python is installed in your system.
So far:

Pulumi is installed
Python is installed
AWS credentials are configured

We are good to write script to provision resources and deploy static website on S3.

1 pulumi new is a command to create a new pulumi project with some basic scaffolding.

mkdir hugo-pulumi && cd hugo-pulumi
pulumi new aws-python

2 You will be asked for a project name and project description. Hit Enter to accept the default values or change if required.

Pulumi code is organized in stack and projects. Project is a folder with all code and stack is instance of that code with different configurations. Single project can have multiple stacks for different environment (dev, test and prod)

3 Lets review the generated files:
Pulumi.yaml: This file contains the metadata and runtime that pulumi will use to execute the scripts. In most of cases we are not going to anything in this file.
Pulumi.dev.yaml: This file contains stack specific configuration. Instead of hardcoding values of different deployment stack (prod, dev, staging) we can add configuration values in this file. Based upon the specified stack Pulumi will pick the configurations.This file is automatically named: Pulumi.<stack-name>.yaml
__main__.py: This is the file where we will define our stack resources.

4 To deploy our static website in S3 we need to perform below steps:

Creating a S3 bucket resource on AWS cloud.
Grant public read access, we want our website public.
Enable static website hosting in S3 bucket and configuring index.html file.
Build Hugo website and upload website files.

5 Edit __main__.py file.

Create bucket.

bucket = s3.BucketV2("hugo-pulumi")

Set bucket ownership control, only bucket owner can upload the object.

ownership_controls = s3.BucketOwnershipControls(
    "ownership-controls",
    bucket=bucket.id,
    rule={
        "object_ownership": "ObjectWriter",
    },
)

Block public access to bucket.

public_access_block = s3.BucketPublicAccessBlock(
    "public-access-block", bucket=bucket.id, block_public_acls=False
)

Mark bucket as static and make index.html as default page.

website = s3.BucketWebsiteConfigurationV2(
    "website",
    bucket=bucket.id,
    index_document={
        "suffix": "index.html",
    },
)

Now bucket is provisioned let's add some files:

file_path = os.path.join(root, file)
relative_path = os.path.relpath(file_path, folder_name)
mime_type, _ = mimetypes.guess_type(file_path)
s3.BucketObjectv2(
    relative_path,
    bucket=bucket.id,
    source=pulumi.FileAsset(file_path),
    key=relative_path,
    content_type=mime_type,
    acl="public-read",
    opts=pulumi.ResourceOptions(
        depends_on=[public_access_block, ownership_controls, website]
    ),
)

file is the file object. We will attach the acl policy and ownership controls with file object and use s3.BucketObjectv2 function to upload the file to bucket.

6 Deploy your stack:

pulumi up

This command will evaluate your program and determine resource updates to make. It will first show you the preview and take confirmation from you before executing the changes. If you follow all the steps correctly then you should be able to see the public url of your bucket and access the website.

7 You can use this command to verify the objects uploaded to S3:

aws s3 ls $(pulumi stack output bucket_name)

Congrats you have successfully used Pulumi to upload the file to S3. You can refer the complete example in following link:

shivamarora1 / hugoXPulumi

Deploy static site generated by Hugo using Pulumi

This repository is a submission for the Pulumi Deploy and Document Challenge.
You can read the full submission article here.

Make sure you have setup following environment variables:

export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
export AWS_REGION=ap-south-1

cd pulumi-deployment

Make sure pulumi is installed in your system.

brew install pulumi/tap/pulumi

Apply the provisionings

pulumi up

Open the url in your browser.

View on GitHub

8 Use pulumi destroy to delete the provisions.

Khabar Suno: Multi Lingual News website

Shivam — Mon, 24 Mar 2025 06:10:50 +0000

This is a submission for the KendoReact Free Components Challenge.

What I Built

The English translation of Khabar Suno is "Listen News." This news website features content in both English and Hindi, and you can change the language according to your preference. Additionally, if you're in a hurry, there is a feature that allows you to listen to all the latest news in just one minute.

Demo

👨‍💻 Repository:

shivamarora1 / Khabar-suno

Read and Listen latest news in english and hindi language.

The English translation of Khabar Suno is "Listen News." This news website features content in both English and Hindi, and you can change the language according to your preference. Additionally, if you're in a hurry, there is a feature that allows you to listen to all the latest news in just one minute.

Running Project

npm run dev

Open http://localhost:3000 with your browser to see the result.

View on GitHub

📄 Live Demo: https://khabar-suno.vercel.app/

🖥️ Screenshots:

KendoReact Experience

I used the following components for this mini project:

Button
Icon
Skeleton
Card
Tooltip
Typography
Drop Down List
AppBar
Drawer
Dialog Box

Coming from a backend development background, I found it very easy to learn how to develop a frontend UI using KendoReact. The component documentation is thorough and well-written. You can find a wide range of components to build a complete, end-to-end SaaS product. Additionally, the styling and themes offer a significant advantage. Overall, great work, team KendoReact!

AIm to Impress

Leveraging advanced generative AI, I automatically generate comprehensive summaries of the latest news updates from today. These summaries are then transformed into high-quality audio using cutting-edge OpenAI models, enabling users to conveniently listen to the news on the go. This innovative approach not only streamlines the news consumption process but also enhances accessibility, ensuring that users can stay informed no matter where they are.

Models used:

gpt-4o-mini-tts : For text-to-speech.
gpt-4o-mini : For text summarization.

YouTube Summariser: Extract essence of YouTube videos.

Shivam — Mon, 15 Apr 2024 07:23:47 +0000

This is a submission for the Cloudflare AI Challenge.

What I Built

Are you tired of watching long YouTube videos and wish there was a faster way to get the key points? Look no further! With our tool, you can simply input the YouTube video link, and we'll generate an enriched summary of the video for quick consumption.

Demo

https://worker-bold-smoke-3cc1.shivamarora1.workers.dev/

My Code

shivamarora1 / ai-youtube-summarizer

AI based tool to summarise youtube videos

AI Youtube Summarizer

No more wasting time on lengthy videos when you just need the highlights.

Steps to run it.

Install all required dependencies.

npm install

Run application.

npm run dev

View on GitHub

Journey

I downloaded Youtube videos transcriptions from the api and used @cf/facebook/bart-large-cnn model to summarise the content.

KYC automation using Azure AI

Shivam — Tue, 08 Mar 2022 21:59:03 +0000

Overview of My Submission

Leveraging Azure AI form recognizer cognitive service to automate customer KYC verification and validation process. Using this project submission, manual reviewers do not need to analyse and go through each and every submitted document by the customers. The AI service will reject invalid and bogus KYC documents.

👉 Lets have a brief knowledge about KYC and legacy KYC verification flow.

What is KYC?

Businesses need to ensure customer due diligence (CDD) using know your customer (KYC) documents. CDD involves verifying that customers are who they say they are and assessing the risks like the possibilities of fraud, money laundering, terrorism financing etc.
KYC process includes verifying customer’s name, address, photograph by analysing bank documents, utility bills, etc.

Legacy KYC process 🛠

Customer need to upload his KYC document like PAN, Utility bills etc. through an online portal.
Manual reviewer reviews each and every document uploaded by customer and verifies the following checks:

Integrity of uploaded KYC document.
The image quality of KYC document.
Validating information(Name, Father Name, DOB, Document number) in KYC document.

Azure AI based implementation:

Customer need to upload his KYC document like PAN, Utility bills etc. through an online portal.
After receiving KYC document, custom trained form recognizer model with run and validate:

Structure (template) of KYC document.
Extract data from KYC document.
Then according to business logic, extracted data/ fields from KYC document are verified.

Submission Category:

AI Aces, Computing Captains

Resources Used:

Azure AI Form Recognizer (For custom machine learning model).
Azure Functions (For front end api and business logic layer).

Link to Code on GitHub

shivamarora1 / kyc-automation-azure-trial-hackathon

KYC automation using Azure cloud AI service

Overview of My Submission

Backend API

# API to submit KYC document for analysis. 
curl --location --request POST 'https://pan-kyc-automate-1234009.azurewebsites.net/api/kyc-doc-upload' \
--form 'file=@"jS7VOIp6r/image.png"'

# After submitting document in step 1, you will get requestId in response, You have to use that with other informations that you want to validate.
curl --location --request POST 'https://pan-kyc-automate-1234009.azurewebsites.net/api/kyc-doc-verify' \
--header 'Content-Type: application/json' \
--data-raw '{
    "name": "D MANIKANDAN",
    "fatherName": "DURAISAMY",
    "pan": "BNZPM2501F",
    "dob": "16/07/1986",
    "requestId": "3c89950b-d282-46c1-9c54-fb565fc1ab20"
}'

Since I am using Azure trial account, so above backend apis will stop working very soon

Frontend

👉 go to this static page: ./frontend/index.html

Cloud diagram

Reference:

View on GitHub

Screen shot

Customer submitting KYC documents

Screen after waiting for KYC repsonse

Architecture:

References:

Use GitHub actions to auto tag your repo

Shivam — Tue, 25 Jan 2022 15:59:58 +0000

In this post we will learn how we can leverage GitHub Action to tag the repository whenever there is a push on the main branch.

Normally developers tend to forget tagging the repository whenever some new stable code is merged on the main branch and eventually deployed on production. This workflow specified in this post aims to automate this tagging process.

Actions used in this blog can be find on:

shivamarora1 / gitaction_tag

Make a tag whenever there is push to the master branch

This repository contains a GitHub Action which will tag the repository whenever there is a push on the main branch. Normally developers tend to forget tagging the repository whenever some new stable code is merged on the main branch and eventually deployed on production. This workflow aims to automate the tagging process whenever there is a new deployment on main branch.

Some background on Git tag and GitHub actions

Git Tag

Tags are like way to create a snapshot of Git repo. Git tags are reference that point to specific points in the Git history. A tag is a branch which never change. Tags are generally marked as semantic versioning (v1.2.3) .

Creating a tag:

git tag <tag_name>

Publishing a tag:

git push origin <tag_name>

More info on tagging

GitHub Actions

GitHub Actions is a platform that helps to automate the processes in software build, test, and…

View on GitHub

Some points used in above repository:

Each running GitHub action has a unique run id that can be accessed using github context : ${{github.run_id}}
uses: actions/checkout@v2 This step is used to checkout latest copy of your repository.
This GitHub Action is executed whenever there is push event on main branch.

Event emails reminder using Mongo DB Atlas

Shivam — Fri, 14 Jan 2022 00:03:54 +0000

Overview of My Submission

The main aim of this project is to fire an email to the user with an appropriate description when the time of the particular event (set by the user prior) is up.

Working

1.The events are stored in documents. Each event has keys: description, time, processed, emailId.

Index is used to make query process fast
db. remindersList.createIndex({ processed: 1, date: 1 })

2.The time (in time field) controls when the event will be fired. The scheduled trigger type is deployed which checks what event has been passed in regular intervals.

3.The event occurrence, email is fired to email id (in emailId field) with description (in description field). For sending email, I am using 3rd party dependency SendGrid.

4.After the event is processed, the processed field is set true, so that event is not fired again.
context.services.get("mongodb-atlas").db("reminders").collection("remindersList").updateOne({_id:id},{$set:{processed:true}})

Submission Category:

Choose Your Own Adventure

Atlas services used:

Atlas Database
Realm Triggers
Realm Functions

Link to Code

shivamarora1 / mongodb-realm

Email Reminder Service Mongo DB Realm application

Submission for MongoDB Atlashackathon

The main aim of this project is to fire an email to the user with an appropriate description when the time of the particular event (set by the user prior) is up.

Working

The events are stored in documents. Each event has keys: description, time, processed, emailId.

Index is used to make query process fast db. remindersList.createIndex({ processed: 1, date: 1 })

The time (in time field) controls when the event will be fired. The scheduled trigger type is deployed which checks what event has been passed in regular intervals.

The event occurrence, email is fired to email id (in emailId field) with description (in description field). For sending email, I am using 3rd party dependency SendGrid.

After the event is processed, the processed field is set true, so that event is not fired again context.services.get("mongodb-atlas").db("reminders").collection("remindersList").updateOne({_id:id},{$set:{processed:true}})

Submission Category:

Choose…

View on GitHub

Additional Resources / Info

Bug notification on Slack

Shivam — Wed, 08 Dec 2021 11:21:54 +0000

Instructions

Following repository contains the GitAction which triggers the work flow(sending notification to the slack) whenever an issue with label bug 🐞 is raised. The issue of type bug are one that require high attention of project owner and required to be fixed as soon as possible.

My Workflow

Link to my submission.

Submission Category:

Maintainer Must-Haves, Wacky Wildcards

Yaml File or Link to Code

shivamarora1 / gitactions-slack-notification-bug-issue-raised

Submission for GitAction Hackathon 2021

Project for GitAction Hackathon 2021.

Introduction:

This repository contains the GitAction which triggers the work flow(sending notification to slack) whenever a issue with label bug is raised. The issue of type bug are one that require high attention of project owner and required to be fixed as soon as possible.

My Workflow

The workflow is triggered whenever event=issues with types=opened is happened.
If expression is used to check whether this issue contains label bug or not.
If the above check passes then it stores the various meta values like issue raiser name, repository url, issue url and issue title
After getting the neccessary data the message on slack channel is sent using slackapi/slack-github-action@v1.16.0 action.

Submission Category

Maintainer Must-Haves, Wacky Wildcards

YAML File

LINK

Additional Resources

Screen shot of alert send on slack

LICENCE

MIT

View on GitHub