ReadME: Rate limit monitor action badge updated

README.md

@@ -6,6 +6,7 @@
 [![github-repo-stats](https://github.com/sayantikabanik/DataJourney/actions/workflows/github-repo-stats.yml/badge.svg)](https://github.com/sayantikabanik/DataJourney/actions/workflows/github-repo-stats.yml)
 [![Deploy DataJourney Stats](https://github.com/sayantikabanik/DataJourney/actions/workflows/static.yml/badge.svg)](https://github.com/sayantikabanik/DataJourney/actions/workflows/static.yml)
 [![Lint prose](https://github.com/sayantikabanik/DataJourney/actions/workflows/review.yml/badge.svg)](https://github.com/sayantikabanik/DataJourney/actions/workflows/review.yml)
+[![Monitor GitHub API Rate Limit](https://github.com/sayantikabanik/DataJourney/actions/workflows/rate-limit-monitor.yml/badge.svg)](https://github.com/sayantikabanik/DataJourney/actions/workflows/rate-limit-monitor.yml)
 
 </h1>
 
@@ -24,7 +25,7 @@
 
 DataJourney demonstrates how organizations can effectively manage and utilize data by harnessing the power of open-source technologies. It's designed to help navigate the complex landscape of data tools, offering a structured approach to building **scalable**, and **reproducible** data workflows.
 
 Built on open-source principles, the framework guides users through essential steps—from **identifying** goals and **selecting tools** to **testing** and **customising** workflows. With its flexible, modular design, DataJourney can be tailored to individual needs, making it an invaluable toolkit for data professionals.
 
 ### 🧱 Design Philosophy (LEGO)
 Built with additive, subtractive capabilities glued with open source.
@@ -32,7 +33,7 @@
 
 - PO (Base): Static home(s) to keep it together `(GitHub)`
 - P1 (Tooling): Tooling, strings `(Powered by open source)`
 - P2 (Maintenance + Monitoring): Env, automations `(Pixi + GHA)`
 - P3 (Abstraction): Layer(s), CLI/task manager for users to interact with `(Pixi)`
 
 
@@ -47,11 +48,11 @@
 ✅ `Vale.sh` configured at PR level\
 ✅ `Pre-commit hooks` configured for code linting/formatting\
 ✨ [LangChain](https://python.langchain.com/) Basics & workflows\
 ✅ Environment management via [pixi](https://prefix.dev/)\
 ✅ Reading data from online sources using [intake](https://github.com/intake/intake)\
 ✅ Sample pipeline built using [Dagster](https://github.com/dagster-io/dagster)\
 ✅ Building Dashboard using [holoviews](https://holoviews.org/gallery/index.html) + [panel](https://panel.holoviz.org/reference/index.html)\
 ✅ Exploratory data analysis (EDA) using [mito](https://www.trymito.io/)\
 ✅ Web UI build on [Flask](https://flask.palletsprojects.com/en/3.0.x/) \
 ✅ Web UI re-done and expanded with [FastHTML](https://docs.fastht.ml/)\
 ✅ Leverage AI models to analyse data [GitHub AI models Beta](https://docs.github.com/en/github-models/prototyping-with-ai-models)
@@ -62,8 +63,8 @@
 - Generate & add `GITHUB_TOKEN`, instructions [here](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token-classic)
   - Added requirement to run the LLM workflows
 - Switch directory `cd DataJourney`
 - Download pixi : [prefix.dev](https://prefix.dev/)
 - Activate env: `pixi shell`
 - Install DJ framework locally `pixi run DJ_package`
 - List all the tasks: `pixi task list`
 - Execute a task from the list: `pixi run <TASK>`
@@ -72,18 +73,18 @@
 ### 🏃🏽‍♀️ Active `tasks` under DJ
 
 - GIT_TOKEN_CHECK
 - DJ_package
 - DJ_pre_commit
 - DJ_dagster
 - DJ_fasthtml_app
 - DJ_flask_app
 - DJ_mito_app
 - DJ_panel_app
 - DJ_llm_analysis
 
 
 ### 🔌 About pre-commit-hooks and activating
 Just like the name suggests, pre-commit-hooks are designed to format the code based on PEP standards before committing. [More details](https://pre-commit.com/)
 
 ```shell
 pixi run DJ_pre_commit
@@ -95,12 +96,12 @@
 pixi run DJ_llm_analysis
 ```
 
 ### 🪼 Execute pre-configured Dagster pipeline
 
 ```shell
 pixi run DJ_dagster
 ```
 ![Dagit UI output](assets/pipeline/dagster_ui.png)
 
 ### 🐙 Panel app
 ```shell
@@ -108,11 +109,11 @@
 ```
 
 *NOTE:*
 The dashboard generated is exported into HTML format and saved as [stock_price_twilio_dashboard](analytics_framework%2Fdashboard%2Fstock_price_twilio_dashboard.html)
 
 ![Panel app output](assets/dashboard/panel_app_stock.png)
 
 ### 🐵 Mito
 
 To explore further visit [trymito.io](https://docs.trymito.io/)
 ```shell
@@ -122,8 +123,8 @@
 [//]: # (![mito output]&#40;assets/pipeline/mito_graph.png "Graph generated via mitosheet"&#41; ![mito output operation]&#40;assets/pipeline/mito_operations.png "Operations performed via mitosheet"&#41;)
 
 <div style="display: flex; justify-content: space-between;">
     <img src="assets/pipeline/mito_graph.png" alt="mito_output" width="400"/>
     <img src="assets/pipeline/mito_operations.png" alt="mito_output" width="400"/>
 </div>
 
 ### 🦋 Display all data sources present via web UI