Libraries tagged by ISVU

Connects your TYPO3 website with SYSSY for monitoring and security issue reporting. Requires account on https://www.syssy.net.

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Provides a Generic Issue Import Format report output for PHPCS, for use with tools such as Sonarqube

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A Magento 2 module for issue #14952

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This Composer package provides a seamless integration for testing SEO aspects of your Laravel applications. Compatible with both Pest and PHPUnit, it offers a collection of tools and assertions specifically designed to evaluate on-page SEO elements like meta tags, title tags, canonical URLs, and structured data. By automating SEO testing, this plugin ensures that your application consistently adheres to best SEO practices, helping you catch potential SEO issues early in the development cycle.

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PetaLog is a powerful log and exception aggregation tool designed specifically for Laravel projects. It seamlessly captures exceptions from your Laravel applications and centralizes them in a user-friendly interface, allowing developers to efficiently monitor, analyze, and resolve issues.

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Fixes WordPress issues with home and site URL on multisite when using Bedrock

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Issue HTTP request.

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Issue HTTP request.

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Workarround to the issues related of using Doctrine with embedabbles relations where the relation can be null. If the object is null doctrine would instanciate it with all the properties to null, this library will fix it to set it to null.

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Provides single-sign-on ability to Microsoft Azure Active Directory enabled apps. This package is updated to work with Laravel 8, as the forked package had depency issues with guzzle.

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eMailTest is a Moodle local plugin that can help diagnose SMTP email connectivity issues.

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Mark-a-Spot is a Drupal distribution for crowdmapping and public civic issue tracking

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With Magento 2 FAQ Extension, you will not need to worry about creating useful and nice FAQ page for your ecommerce website. The extension will help you to set up nice informative & SEO-friendly magento 2 FAQ page. With faq page, your customers will find convenient, easily and quick to resolve common issues.

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Automatically detects issues from browser on site and notifies by email, chat or ticket system via Bugsnag.

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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