Libraries tagged by site structure

Lists or flushes the site's rewrite rules, updates the permalink structure.

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Imports records from a Yaml file structure into the TYPO3 database

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Generates an llms.txt file according to the llmstxt.org specification and serves pages as Markdown, giving Large Language Models a structured, machine-readable view of your site.

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Commercial flat-file PHP CMS for web professionals: structured content modeling, no database, content stored as JSON on disk, Twig templating, a REST API, a CLI, and a built-in MCP server so AI agents can work with a live site.

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The storage glue of omnibase sites, framework-free: Doctrine reading the structure described outside of it (omnisense/omnibridge's Omnibridge\Schema), the naming strategy that turns their entities into their tables, the UTC date types, and an entity manager built with them, for any framework reading the same database; and omnibase's persistence on Doctrine (Omnibridge\Doctrine\Omnibase\: its repositories, finder compiler, mapping, DBAL types, walkers, filters and listeners), which omnisense/omnibase requires.

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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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Kirby Super Structure: a structure field with per-field translate: false support and stable row UUIDs for multi-language sites.

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Customizes status tests that conflict with the way Bedrock is structured.

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Content engine for Laravel sites: structure in code, content in the database, rendering with the site's own components.

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Adds Schema.org information into your site.

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Reusable countries and structured postal addresses for Capell: one shared address record, site-scoped, that any package can reference instead of re-modelling location fields.

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Starter sites for sunrice/cms: install a whole site (structure, sample content, media, templates and assets) in one command, and make your own from a site.

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Automatic organization of uploaded files based on the site tree structure.

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A parsing library for parsing pages from a few common non-mf2 sites into the mf2 structure

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Drush commands to export D7 site structure.

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