A Cross Script Validator, node, browser, java
Express cross validator
Validate identifier/keywords name
Another JSON Schema Validator
Validate plugin/preset options
Additional JSON-Schema keywords for Ajv JSON validator
Decorator-based property validation for classes.
Cross platform child_process#spawn and child_process#spawnSync
String validation and sanitization
Another JSON Schema Validator
Ajv class for JSON Schema draft-04
Express middleware for the validator module.
Object schema validation
Custom error messages in JSON Schemas for Ajv validator
Validate form asynchronous
validate form asynchronous
Parser/validator for OpenAPI 3.x definitions
TypeScript definitions for validator
Run scripts that set and use environment variables across platforms
Universal WHATWG Fetch API for Node, Browsers and React Native
Format validation for Ajv v7+
Automatically validate API requests and responses with OpenAPI 3 and Express.
A library for validating credit card fields
The ajv-8 based validator for @rjsf/core
Gradient Boosted Regression Trees in Rust
Lightweight regression library (OLS, Ridge, Lasso, Elastic Net, WLS, LOESS, Polynomial) with 14 diagnostic tests, cross validation, and prediction intervals. Pure Rust - no external math dependencies. WASM, Python, FFI, and Excel XLL bindings.
Model selection utilities for the ferrolearn ML framework
Parse any tensor format, recover any precision — framework-agnostic FP8/GPTQ/AWQ/BnB dequantization, NPZ parsing, and PyTorch .pth conversion for Rust
Unified spatial statistics library: kriging, spatial autocorrelation, spatial regression, point process analysis
FFI-free Rust implementation of LIBSVM-compatible SVM training and prediction
Model selection utilities for sklears: cross-validation, grid search, train-test split
Time series forecasting library
Tensor decompositions: CP-ALS, Tucker-HOOI, TT-SVD
Pure-Rust ext4 filesystem driver. Exposes a C ABI (fs_ext4_*) suitable for FFI from C/C++/Go/etc.
A Rust port of dagre - directed graph layout using the Sugiyama method
Reference 5th-implementation runner for JCS (RFC 8785) preimage discipline in x402 STARK Receipt Format Extension (draft-vauban-x402-stark-receipts-00). Cross-validates byte-identical canonical bytes against publicly available JCS conformance suites.
This gem is very fast C++ code for calculating AUCs on results of cross-validation. It is specific to the crossval database schema, which has not been released yet. Chances are you will not find this very useful unless you are the author. It is in gem form to ensure that each lab machine can compile its own arch-specific version.
Performs k-fold cross-validation on machine learning classifiers.
Rumale::ModelSelection provides model validation techniques, such as k-fold cross-validation, time series cross-validation, and grid search, with Rumale interface.
A cross-platform Pact verification tool to validate API Providers. Used in the pact-js-provider project to simplify development
A cross-platform Pact verification tool to validate API Providers. Used in the pact-js-provider project to simplify development
Create K-fold splits from data files and assist in training and testing (useful for cross-validation in supervised machine learning)
It provides basic command line tools for simply defining things like cross validations, factorial experimental design and basic statistics. All of this can be run in a distributed manner.
Cross-origin resource sharing (CORS) is great; it allows your visitors to asynchronously upload files to e.g. Filepicker or Amazon S3, without the files having to round-trip through your web server. Unfortunately, giving your users complete write access to your online storage also exposes you to malicious intent. To combat harmful usage, good upload services that allow client-side upload, support a mechanism that allows you to validate and sign all upload requests to your online storage. By validating every request, you can give your visitors a nice upload experience, while keeping the bad visitors at bay. The CORS gem comes with support for the Amazon S3 REST API.
rails-ai-context turns your running Rails app into the source of truth for AI coding assistants. Instead of guessing from training data or stale file reads, agents query 38 live tools (via MCP server or CLI) to get your actual schema, associations, routes, inherited filters, conventions, and test patterns. Semantic validation catches cross-file errors (wrong columns, missing partials, broken routes) before code runs — so AI writes correct code on the first try. Auto-generates context files for Claude Code, Cursor, GitHub Copilot, OpenCode, and Codex CLI. Works standalone or in-Gemfile.
Ukiryu is a platform-adaptive command execution framework that transforms CLI tools into declarative APIs. It provides the "OpenAPI" for command-line interfaces, enabling cross-platform tool integration with type safety and structured results. Key features: * Declarative YAML profiles define tool behavior, eliminating hardcoded command strings * Platform-adaptive execution across macOS, Linux, and Windows * Shell-aware command formatting for bash, zsh, fish, PowerShell, and cmd * Type-safe parameter validation with automatic coercion * Version routing support with semantic version matching (via Versionian) * Interface contracts allow multiple tools to implement the same abstract API * Structured Result objects with success/failure information instead of parsing stdout * Comprehensive error handling under Ukiryu::Errors namespace The Ukiryu ecosystem consists of: * ukiryu gem - The runtime framework * ukiryu/register - Collection of YAML tool profiles * ukiryu/schemas - JSON Schema for validation Use Ukiryu to integrate command-line tools like ImageMagick, FFmpeg, Inkscape, Ghostscript, and more into your Ruby applications with consistent, predictable interfaces.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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