{"skills":[{"name":"Leadership","esco_label":"leadership","type":"soft","confidence":0.95},{"name":"Mentoring","esco_label":"mentoring","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'
Após se cadastrar, cada desenvolvedor recebe uma chave de acesso à API pessoal, uma combinação única de letras e dígitos para acessar nosso endpoint de API. Para autenticar com a Multilingual Skills Extraction API basta incluir seu token Bearer no cabeçalho Authorization.
| Cabeçalho | Descrição |
|---|---|
Authorization
|
Obrigatório
Deve ser Bearer access_key. Veja "Sua chave de acesso à API" acima quando você estiver inscrito.
|
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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.
Os usuários podem personalizar suas solicitações de dados fornecendo entrada de texto bruto na solicitação POST para o endpoint /parse-skills A API processa automaticamente o texto sem exigir parâmetros adicionais ou pré-processamento
Os casos de uso típicos incluem a melhoria dos sistemas de rastreamento de candidatos (ATS) a melhoria do correspondência entre candidatos a emprego a automação da extração de currículos a realização de análises de talentos e a simplificação dos processos de recrutamento para profissionais de RH
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.