🧠 The production-ready cognitive foundation for autonomous systems such as OpenClaw and Embodied-AI. For memory management, from extraction and search to automated optimization, with SKILL, CLI, API, MCP, and insights dashboard out-of-the-box.

Explore a collection of end-to-end data analytics projects showcasing SQL, Python, and Power BI. Gain valuable insights and solutions to real-world problems through data extraction, analysis, and visualization. Ideal for beginners and professionals looking to enhance their skills in data analytics.

A collection of Claude Code / Codex AGENTIC SKILLs for document manipulation - PDF extraction/forms, Excel analysis/formulas, Word, and PowerPoint

A collection of OpenClaw Agent Skills — search, analysis, content extraction, and more.

Image Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree

24 AI Agent Skills for professional security auditing of Supabase applications. Detection, key extraction, RLS testing, storage audit, IDOR detection, and comprehensive reporting. Works with Claude Code, Cursor, Windsurf, and 30+ AI agents.

Extracting Skills from resume using Machine Learning

Automatic collection of posted LinkedIn jobs appearing in a specific search, and extraction of the required skills based on all the job descriptions.

This repository hosts a custom NER model developed using spaCy3. The NER model is designed to automatically extract crucial information such as names, skills, experience, and education from resumes. The integration of this NER model into a seamless pipeline ensures efficient parsing and structured data extraction for resume Analysis

A Python-based tool that automates the extraction and visualization of skills from CVs using OCR technology. Ideal for streamlining candidate evaluation processes in recruitment.

A collection of reusable command-line skills for web scraping, data extraction, and automation tasks

A collection of my Python projects, including web scraping, automation scripts, and data extraction tools. Showcasing my skills in BeautifulSoup, Selenium, and API integration

A systematic exploration of Computer Vision fundamentals using OpenCV and Python. This repository documents my learning journey, featuring well-documented scripts for core CV tasks, from image manipulation to advanced feature extraction. Designed as a practical showcase of foundational computer vision skills.

app for resume skills extraction and position of applicant

Dataset of synthetic job ad sentences tagged with ESCO skills. From the paper Extreme Multi-Label Skill Extraction Training using Large Language Models.

Rakesh Men's collection of SQL-based data analysis projects showcasing query skills, insights extraction, and data-driven decision-making.

Welcome to my n8n Automation Workflows Showcase — a curated collection of automation workflows demonstrating my skills in web scraping, workflow automation, data extraction, and productivity solutions.

SimpleResumeParser is a lightweight resume parsing framework that extracts names, emails, and skills from PDF or DOCX resumes using a combination of LLMs (RAG), HuggingFace NER models, rules-based logic, and regex patterns. It’s designed with pluggable architecture for production-ready, structured data extraction.

This repository introduces a multi stage pipeline for skill extraction. The pipeline includes a curriculum bi-encoder, trained to retrieve relevant skill candidates for job description sentences. Then in Stage 2 a cross-encoder model is trained with a binary ranking objective, to determine which of the retrieved candidates are true.

Web scraping multi-page data from e-commerce website 'JD Sports'. Highlighting skills in automated data extraction, cleaning, and preprocessing of large datasets

Automates extraction of property deed and tax info using Selenium. Handles CAPTCHAs, stale elements, and inconsistent PDFs with robust error handling. Includes automated PDF downloads and screenshots for debugging. Built to deepen my skills in automation and data extraction workflows.

🎯 --- Web Scraping Challenge Game --- A series of progressively difficult web scraping exercises to master BeautifulSoup4 and data extraction techniques. Test your skills with real-world HTML structures.

This LinkedIn web scraping project automates the extraction of profile data from LinkedIn user profiles. It enables the collection of valuable information such as names, job titles, education details, skills, and contact information.

A mission-critical collection of AI-native agents for Design System governance. Automate Figma variable extraction, token gap analysis, WCAG auditing, architectural refactoring, and multi-platform code synchronization. Designed for modular Vercel Skills (skills.sh) implementation.

This project is part of Eskwelabs Data Science Fellowship Cohort 14 curriculum. This project aims to find critical skills gap of Filipino candidates in data analytics sector jobs through network analysis and keyword extraction techniques.

A collection of SQL projects showcasing skills in data extraction, joins, aggregations, subqueries, window functions and performance optimization. These projects focus on solving real-world business and financial problems using structured databases.

This repository is your gateway to the world of sentiment analysis and feature extraction from product reviews. Explore the power of natural language processing (NLP) and fine-tune your sentiment analysis skills. 🛍️📊 #NLP #SentimentAnalysis #ProductReviews #PrecisionRecall

This project is a Text Extraction and Text Classification project. The model takes input in text format and extracts the skills contained to use as predictors for suitable data-role using the Functional API of Bidirectional LSTM Network.

Zepto E-commerce SQL Data Analyst project showcasing data extraction, cleaning, and analysis of sales, customers, and inventory. Includes complex SQL queries, KPIs, and insights to drive business growth, improve decision-making, and highlight problem-solving skills for real-world analytics.

Collection of robust shell wrappers with unified interface for multiple AI CLIs (Claude, Gemini, Cursor, Codex, and OpenCode). These scripts standardize agent execution by handling persona extraction from Markdown, session persistence, skills, automatic context injection, and process lifecycle management (timeouts/cleanup)