JSON schema: extract a resume

JSON Schema to extract a resume with an LLM: experience, skills, education and certifications. Leaves out age, photo and other protected data.

Extract the resume data. Invent nothing: if information is missing, leave the field empty. Never infer age, race, religion, national origin, marital status, pregnancy or disability: these are protected characteristics under U.S. equal employment law.

JSON Schema

{
  "type": "object",
  "required": [
    "name",
    "experience",
    "skills"
  ],
  "properties": {
    "name": {
      "type": "string"
    },
    "skills": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "headline": {
      "type": "string",
      "description": "Current or target job title"
    },
    "education": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "year": {
            "type": "string"
          },
          "degree": {
            "type": "string"
          },
          "school": {
            "type": "string"
          }
        }
      }
    },
    "experience": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "title",
          "company"
        ],
        "properties": {
          "end": {
            "type": "string"
          },
          "start": {
            "type": "string"
          },
          "title": {
            "type": "string"
          },
          "company": {
            "type": "string"
          }
        }
      }
    },
    "certifications": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "years_of_experience": {
      "type": "number"
    }
  }
}

Coste estimado

Tokens de entrada por llamada : ≈ 554

Solo coste de entrada, sin la respuesta del modelo. Precios públicos estándar de los proveedores (Mistral AI, DeepSeek, OpenAI, Anthropic) convertidos de USD a euros al tipo del BCE del 29 de septiembre de 2026. Estimación: 1 token ≈ 3,6 caracteres.

Identificador

contextetech--extract-resume

Tipo

Esquemas de salida

Archivo

extract-resume.schema.json

Formato

JSON (application/json)

Tamaño

1292 bytes (1,3 KB)

Codificación

UTF-8

Tokens estimados

≈ 359

Idioma

Inglés

Licencia

MIT

Uso comercial

Permitido

Datos personales

Ninguno

Versión

1.0

Publicado el

2 de octubre de 2026 a las 21:54

Actualizado el

2 de octubre de 2026 a las 21:54

Almacenamiento

en base de datos

Usos

0

Me gusta

0

extract-resume.schema.json

{
  "type": "object",
  "required": [
    "name",
    "experience",
    "skills"
  ],
  "properties": {
    "name": {
      "type": "string"
    },
    "skills": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "headline": {
      "type": "string",
      "description": "Current or target job title"
    },
    "education": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "year": {
            "type": "string"
          },
          "degree": {
            "type": "string"
          },
          "school": {
            "type": "string"
          }
        }
      }
    },
    "experience": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "title",
          "company"
        ],
        "properties": {
          "end": {
            "type": "string"
          },
          "start": {
            "type": "string"
          },
          "title": {
            "type": "string"
          },
          "company": {
            "type": "string"
          }
        }
      }
    },
    "certifications": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "years_of_experience": {
      "type": "number"
    }
  }
}

API de Anthropic (Python)

import json, anthropic

schema = json.load(open("extract-resume.schema.json"))
client = anthropic.Anthropic()
resp = client.messages.create(model="claude-sonnet-5", max_tokens=1024,
    tools=[{"name": "extraire", "description": "Renvoie les données extraites", "input_schema": schema}],
    tool_choice={"type": "tool", "name": "extraire"},
    messages=[{"role": "user", "content": "tu entrada aquí"}])
print(resp.content[0].input)

Formato OpenAI (response_format)

{
  "type": "json_schema",
  "json_schema": {
    "name": "extract-resume",
    "schema": {
      "type": "object",
      "required": [
        "name",
        "experience",
        "skills"
      ],
      "properties": {
        "name": {
          "type": "string"
        },
        "skills": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "headline": {
          "type": "string",
          "description": "Current or target job title"
        },
        "education": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "year": {
                "type": "string"
              },
              "degree": {
                "type": "string"
              },
              "school": {
                "type": "string"
              }
            }
          }
        },
        "experience": {
          "type": "array",
          "items": {
            "type": "object",
            "required": [
              "title",
              "company"
            ],
            "properties": {
              "end": {
                "type": "string"
              },
              "start": {
                "type": "string"
              },
              "title": {
                "type": "string"
              },
              "company": {
                "type": "string"
              }
            }
          }
        },
        "certifications": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "years_of_experience": {
          "type": "number"
        }
      }
    },
    "strict": true
  }
}