{"skills":[{"name":"Leadership","esco_label":"Leadership and management","type":"soft","confidence":0.95},{"name":"Mentoring","esco_label":"Mentoring and coaching","type":"soft","confidence":0.95}]}
curl --location --request POST 'https://zylalabs.com/api/13176/multilingual+skills+extraction+api/26723/parse+skills?text=led the team and mentored 3 juniors' --header 'Authorization: Bearer YOUR_API_KEY'
साइन अप करने के बाद, प्रत्येक डेवलपर को एक पर्सनल API एक्सेस की असाइन की जाती है, जो अक्षरों और अंकों का एक यूनिक संयोजन होता है, जिसका उपयोग हमारे API एंडपॉइंट तक पहुंचने के लिए किया जाता है। प्रमाणीकरण के लिए Multilingual Skills Extraction API के साथ बस अपने बेयरर टोकन को Authorization हेडर में शामिल करें।
| हेडर | विवरण |
|---|---|
Authorization
|
आवश्यक
होना चाहिए Bearer access_key. जब आप सब्सक्राइब हों तो ऊपर "Your API Access Key" देखें।
|
कोई लंबी अवधि की प्रतिबद्धता नहीं। कभी भी अपग्रेड, डाउनग्रेड या कैंसल करें। फ्री ट्रायल में 50 रिक्वेस्ट तक शामिल हैं।
(वार्षिक बिलिंग के साथ 2 महीने बचाएँ 🎉)
अग्रणी कंपनियों का भरोसा
Skills Extraction API turns any job posting or resume into a clean, structured list of professional skills, in any language.
Unlike keyword-based parsers, it uses an LLM to understand context, so it catches IMPLICIT soft skills ("led the team"
becomes Leadership, "mentored juniors" becomes Mentoring) and works on non-English text (Korean, Spanish, Japanese, etc.)
where simpler parsers return an empty result.
For each skill you get:
- name: canonical, searchable name (for example "js" becomes "JavaScript", "k8s" becomes "Kubernetes")
- esco_label: standard ESCO/O*NET-style label
- type: "hard" or "soft"
- confidence: a 0 to 1 score
One endpoint: POST /parse-skills with { "text": "..." } returns { "skills": [...] }
Built for ATS enrichment, job-candidate matching, resume parsing, talent analytics, and recruiting automation. No
preprocessing and no model setup: paste raw text, get normalized JSON back.
The API extracts both hard and soft skills from text, including implicit soft skills such as leadership and mentoring, by understanding the context in which they are mentioned.
The API returns a clean, structured JSON format that includes fields such as 'name' (canonical name), 'esco_label' (standard label), 'type' (hard or soft), and 'confidence' (a score from 0 to 1).
The API can be used for ATS enrichment, job-candidate matching, resume parsing, talent analytics, and recruiting automation, making it valuable for HR and recruitment professionals.
The core value proposition lies in its ability to provide accurate, normalized skill extraction from diverse text inputs, enhancing recruitment processes and improving talent matching through structured data.
Any language the underlying LLM understands, which covers most major world languages. Skill names are always normalized to canonical English.
The API returns a structured JSON object containing a list of normalized professional skills extracted from the input text. Each skill entry includes its canonical name, ESCO label, type (hard or soft), and a confidence score.
The key fields in the response data include 'name' (the normalized skill name), 'esco_label' (the standard label), 'type' (indicating if the skill is hard or soft), and 'confidence' (a score from 0 to 1 representing the extraction certainty).
The response data is organized as a JSON object with a 'skills' array. Each element in the array represents a skill, containing fields for name, ESCO label, type, and confidence score, allowing for easy parsing and utilization.
The endpoint provides information on professional skills extracted from job postings or resumes, including both hard skills (like programming languages) and soft skills (like teamwork), along with their respective confidence scores.
उपयोगकर्ता अपने डेटा अनुरोधों को कच्चे पाठ इनपुट को /parse-skills एंडपॉइंट पर POST अनुरोध में प्रदान करके कस्टमाइज़ कर सकते हैं API स्वतः पाठ को संसाधित करता है बिना किसी अतिरिक्त पैरामीटर या पूर्व-संसाधन की आवश्यकता के
सामान्य उपयोग के मामले में आवेदक ट्रैकिंग सिस्टम (एटीएस) को बढ़ाना नौकरी-उम्मीदवार मिलान में सुधार करना रिज्यूमे पार्सिंग स्वचालित करना प्रतिभा विश्लेषण करना और एचआर पेशेवरों के लिए भर्ती प्रक्रियाओं को सरल बनाना शामिल हैं
Data accuracy is maintained through the use of a large language model (LLM) that understands context, allowing it to extract implicit skills effectively. Continuous training and updates to the model help ensure high-quality skill extraction.
If users receive partial or empty results, they should check the input text for clarity and context. Providing more detailed or structured text can improve extraction results, as the API relies on context to identify skills accurately.