Libraries tagged by technical debt

Static analysis for the debt AI coding agents leave behind, on any PHP project: 26 rules, architecture policies and module boundaries you describe, git-diff review, coverage-aware reading order, PHPStan baseline-growth detection, a Rector and Pint fix pass, Pest expectations, CI annotations, agent rulesets, Claude Code hooks and an MCP server. Runs inside PHPStan through the separate heyosseus/phpstan-sloppy extension. Extra rules and dashboards for Laravel. Deterministic, local, no LLM.

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Static analyzer for PHP project archeology. Calculates various metrics for your codebase.

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Make temporary Laravel workarounds expire and fail CI when ignored.

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Scan, score, and report technical debt in your Laravel application.

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Opinionated PHP code-quality analyzer with SARIF output, baselines, and a local dashboard.

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Sloppy's rules inside your PHPStan run: swallowed exceptions, god methods, N+1 queries, placeholder bodies, weakened tests and the rest of the debt AI coding agents leave in PHP, reported as PHPStan errors. Diff mode, editor mode, a quality gate and a result cache included.

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Tiime software's technical debt tracker

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Track your Fixme comments real quick with just a command!

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App that uses a few code health tools to give an estimated technical debt score.

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Track and enforce temporary code and technical debt in Laravel applications.

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AI-powered Laravel code analysis and engineering intelligence toolkit.

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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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# Introduction ## Overview Note: This is the specification of the DP-DHL Group Parcel DE Returns API. This web service allows business customers to create return labels on demand. # Scenarios ## Main Scenario: Creating a returnlabel This is achieved by posting a return order to the URI '/rest/orders'. The service will respond with a return label. ## Querying to get receiver locations The single scenario supported by this service is the determination of the receiver's location. This is achieved by getting a location to the URI '/rest/locations'. The service will respond with a Receiver. # Technical Note on Authorization This API supports __two alternative ways__ to authorize yourself: 1. Combination of Apikey and Basic Authentication which you can provide with every call. 2. OAuth2 Password Flow: After having obtained your access token once, you provide this token as bearer token. You can try it out here. More details can be found when clicking on "Authorize".

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# Introduction ## Overview Note: This is the specification of the DP-DHL Group Parcel DE Returns API. This web service allows business customers to create return labels on demand. # Scenarios ## Main Scenario: Creating a returnlabel This is achieved by posting a return order to the URI '/rest/orders'. The service will respond with a return label. ## Querying to get receiver locations The single scenario supported by this service is the determination of the receiver's location. This is achieved by getting a location to the URI '/rest/locations'. The service will respond with a Receiver. # Technical Note on Authorization This API supports __two alternative ways__ to authorize yourself: 1. Combination of Apikey and Basic Authentication which you can provide with every call. 2. OAuth2 Password Flow: After having obtained your access token once, you provide this token as bearer token. You can try it out here. More details can be found when clicking on "Authorize".

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Laravel package that hides technical error details from end users and emails full reports.

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