This repository is a community-curated directory of public APIs, organized by category in a single README.md file. Each entry includes the API name, description, authentication type, HTTPS support, and CORS status. The primary data artifact is the markdown file itself, which is manually maintained through pull requests. Supporting Python scripts validate the markdown format (alphabetical order, column padding, description length) and check that all listed API links are reachable, with special handling for Cloudflare-protected endpoints. The validation scripts are well-structured, parameterized, and include a comprehensive test suite, making them reusable for any similar markdown-based table validation. The repository is actively maintained with frequent commits and CI workflows, though its Python dependencies are slightly outdated (certifi 2021, requests 2.27.1). The core value is the curated dataset, not a software application.
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.