Lightcast Skills API Alternatives: 6 Options for Skills Extraction
What changed with Lightcast free access, and six skills APIs to use instead, with sourced sizes and prices.
Insights on resume parsing, recruitment technology, AI in HR, and best practices for your hiring workflow.
What changed with Lightcast free access, and six skills APIs to use instead, with sourced sizes and prices.
How AI resume screening works, then a Python screener that scores every CV against reviewed criteria and explains each requirement.
CEFR, LinkedIn and ILR language levels side by side, test scores converted to CEFR, and how to write them so people and parsers read them.
Free online parsers, free API tiers and open-source libraries, checked on their own pages, with the catch for each one.
Field parsers and criteria extractors for job ads, compared on output, languages and published prices.
Public skills taxonomies and normalization APIs side by side, with sourced sizes, licences and prices.
Stateless or index-based, explained or not: eight matching APIs compared on sourced prices and outputs.
Every recruiting MCP server we could verify: parsing and matching, ATS connectors and job-seeker tools, with sign-in and pricing.
Six resume parsers compared on published prices, output format, connectors and security, with who each one fits best.
What a resume parser does, a CV-to-JSON example, how parsing works step by step, why it fails and how to choose software or an API.
Send a CV to the parser API from PHP 8.2, map the JSON to readonly classes, handle each error code and retry only what is safe.
Upload a CV with HttpClient and MultipartFormDataContent, deserialize it into C# records, and set up safe retries in ASP.NET Core.
Build a multipart request with the JDK HttpClient, map the JSON to records with Jackson, and plug the client into Spring Boot.
Key dates after the Digital Omnibus, how to classify a recruiting tool, and what vendors and employers need by 2 December 2027.
Compare PDF text extraction, OCR, and parser APIs, then follow working Python and Node.js examples for structured resume data.
A hands-on integration guide covering server-side requests, response mapping, error handling, and candidate data protection.
Design a queue-based processing pipeline with durable jobs, bounded retries, duplicate protection, monitoring, and credit controls.
Compare published pricing models and calculate an effective cost per 1,000 successful parser calls from current plans and credits.
Compare public specifications and build a reproducible benchmark for extraction quality, latency, failures, and cost.
Score each field on a labeled test set from your own CVs, find what causes parsing errors, and catch them in production.