92 skills found · Page 2 of 4
niaka3dayo / Agent Skills Vrc UdonSkills, rules, and validation hooks that teach AI coding agents to generate correct UdonSharp code (VRChat SDK 3.7.1–3.10.2)
MitchellkellerLG / Research Process BuilderBuild validated web research processes through self-annealing loops. 138 patterns tested, 90%+ accuracy. Works with Claude Code, Clay, any AI agent.
microsoft / Agent ForgeAGENT-FORGE is a Context Engineering Toolkit that generates GitHub Copilot customization files for your VS Code project. Instead of manually authoring .github/ configuration, you describe what you need and a multi-agent AI pipeline plans, generates, validates, and installs everything.
snakeying / TextumStructured workflow that stops AI from forgetting your requirements. 4 phases with validation gates. Not smarter AI, just controllable process. Weave ideas into code that actually works.
mudigosa / Image ClassifierImage 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
coleam00 / AI Coding Summit Workshop 2Exercises and resources for the AI Coding Summit 2026 workshop: Advanced Claude Code Techniques for 2026. A hands-on two-hour workshop on the PIV Loop (Plan, Implement, Validate) for agentic coding.
orangebread / Speclinter MCPAI-powered specification analysis tool that converts requirements into structured tasks and validates code implementation against original specs.
KubeRocketCI / KuberocketaiDeclarative agentic framework for AI-driven software development. Define, validate, and orchestrate AI agents as code—transparent, auditable, and CI/CD-ready. Run your SDLC as Code today
talvinder / Carrot AI PmCarrot auto-writes specs and catches AI code drift. MCP server for Cursor that AST-validates every commit.
jcputney / Agent Peer ReviewA Claude Code plugin that validates Claude's work using OpenAI Codex CLI. Two AI perspectives catch more issues than one.
mikaelj / FuskaAI project management for solo agentic development. Persistent memory, expert plan validation, automated code review, and structured workflows — powered by a knowledge graph. Works with OpenCode and Claude Code.
motiful / Skill ForgeSkill engineering methodology and publishing pipeline for AI agent skills. Validates structure, scans for security, audits entire projects, and publishes to GitHub. Skills are code — engineer them like it.
frmoretto / HardstopDon't let AI destroy your hard work! HardStop is a rock-solid protection for AI-generated commands. Pre-execution safety validation for Claude Code, Claude Cowork. Catches dangerous commands before they run: whether from AI mistakes, hallucinations, prompt injection, or misunderstood instructions. Seatbelts for the agentic AI era.
Synta-ai / N8n MCP Rulesn8n mcp rules provdes pre-configured prompts that show AI coding agents how to use Synta MCP for n8n workflow automation, which enables AI models to build, validate, & debug n8n workflows with deep knowledge of the platform's complete node library, allowing smart workflow-creation real-time error checking & expert troubleshooting assistance
agenticcontrolio / Twincat Validator MCPAn MCP server that validates, auto-fixes, and scaffolds TwinCAT 3 XML files. Connect it to any LLM client to give your AI assistant reliable, deterministic TwinCAT code quality tooling — structural checks, 21 IEC 61131-3 OOP checks, auto-fix pipelines, and canonical skeleton generation.
Unfold-Security / Pydantic CollabMulti-agent orchestration framework powered by Pydantic AI. Build complex agent collaborations with clean, declarative code, automatic topology validation, built-in execution tracking, and full control over agent interactions, observability included.
savannah-i-g / DryDockDryDock is a framework that creates specialized AI agents for business and productivity roles. It acts as an intelligent "shipyard" where agents are designed, configured, validated, and deployed - entirely within Claude Code (& soon to be other CLI/Agent based AI Tools).
benvancalster / PerfMeasuresOverviewR code and predictions for the case study from Van Calster et al (Validation Studies of Predictive AI for Use in Medical Practice: Overview and Guidance for Performance Measures)
Review-scope / ReviewScopeReviewScope is an open-source AI PR reviewer for GitHub that goes beyond the diff. Uses AST-based analysis and issue validation to deliver low-noise, actionable code reviews. Bring your own API key.
KaushalprajapatiKP / Agentic AI Webapp With Multiple AI Agents And DeploymentA Streamlit webapp deployed on Hugging Face Spaces featuring multiple AI agents built using LangGraph, LangChain, and LLMs. Includes a Startup Idea Validator, Travel Planner, and Agentic AI Chatbot with tools like web search and code execution etc. A modular platform for real-world, graph-driven multi-agent AI interactions.
