This repository is a curated directory of public APIs organized by category. It serves as a reference resource for developers seeking APIs for various functionalities, ranging from advertising and analytics to weather and videogames. Each API entry includes the service name, description, and indicators for open-source or trial-based availability. The primary value lies in the structured, categorized listing of APIs maintained through community contributions. The repository implements basic data quality controls via a Travis CI configuration that uses awesome_bot to validate links in the README, ensuring the directory remains accurate and functional. Contribution guidelines enforce consistent formatting, alphabetical sorting, and proper marking of API types. While not containing complex data processing pipelines, the repository represents a well-maintained dataset with clear governance. Its strength is in the comprehensiveness of the API catalog and the reliability maintained through automated link validation and strict contribution standards. The project follows a typical 'awesome list' pattern common in developer communities for resource discovery.
This repository is a curated directory of public APIs, maintained as a human-readable Markdown table in `README.md` and automatically transformed into structured JSON files (`db/resources.json`, `db/categories.json`) via a Node.js generator. The primary data flow is: community contributions (PRs) update the README table → a GitHub Actions workflow runs `scripts/db/update-db.js` on every push to `main` → the script parses the Markdown using `remark-parse`, extracts categories and API entries, normalizes auth and CORS fields, and writes the JSON output back to the repository. The generated JSON serves as a machine-consumable API catalog for developers and tools (e.g., the companion website publicapis.dev). The repository is fundamentally a data_research project: its core value is the curated dataset of 1,800+ APIs with metadata (name, description, auth type, CORS support, category). The only 'application' component is the lightweight generator script and its utility modules, which are well-structured but tightly coupled to the specific Markdown format. There are no tests, no runtime services, and no external data sources beyond the README itself. The dataset is manually curated via pull requests, which introduces maintenance risks (stale links, incomplete metadata) but also ensures human oversight of quality. For reproducibility, an analyst can clone the repo, run `npm install` (dependencies are locked via `package-lock.json`), and execute `npm run build` to regenerate the JSON from the README. The entire pipeline is self-contained and does not require external credentials or data. However, the lack of unit tests means that changes to the parsing logic could silently break the output.
This repository is a curated 'awesome list' of public security-related APIs, not a software application or data pipeline. The primary artifact is README.md, a hand-maintained table of roughly 100 APIs categorized into Online services, Tools, SIEM platforms, and Various. Each entry records the API name, description, authentication type, HTTPS support, documentation link, and licensing model. The only executable logic is a GitHub Actions workflow that runs awesome_bot weekly to detect broken links in the README. A supplementary document, apidocs/haveIBeenPwned.md, documents unofficial Have I Been Pwned domain-search endpoints discovered in 2018. Despite being classified as an application for tooling purposes, there is no application code, library, or service in this repository. It functions as a knowledge base for security engineers and threat intelligence practitioners. The CI workflow is the only automated component and is a standard awesome-list link checker. The value is in the curation and categorization of APIs, not in reusable software components. The list is actively maintained, but some entries are aging and may reflect outdated API details or vendor changes.