Schema library jobs

Return a salary-data record from a public page.

Pass this JSON Schema and a public source URL to/extract/json. Normalize public salary ranges, location, role, currency, and source date.

Use this schema The JSON file

tabstack.ai/pricing

TEXT

MARKDOWN

LINKS

extract.tsTypeScript

const res = await client.extract.json({
  url: 'https://example.com/pricing',
  json_schema: {
    type: 'object',
    properties: {
      plan: { type: 'string' },
      price: { type: 'number' },
    },
  },
})

res = client.extract.json(
    url='https://example.com/pricing',
    json_schema={
        'type': 'object',
        'properties': {
            'plan': {'type': 'string'},
            'price': {'type': 'number'},
        },
    },
)

tabstack extract json https://example.com/pricing \
  --schema '{"type":"object","properties":{"plan":{"type":"string"},"price":{"type":"number"}}}'

What it captures

Fields in this salary-data record.

Schema for individual salary data points from crowdsourced and published compensation sources.

Field	Type	What it holds
`job_title`	string, required	Job title or role this salary corresponds to.
`company_name`	string	Name of the employer.
`company_domain`	string	Website domain of the employer.
`company_size_range`	string	Company size range (e.g., '51-200', '1000+').
`industry`	string	Industry sector of the employer.
`location_city`	string	City of the job location.
`location_state`	string	State or province of the job location.
`location_country`	string	Country of the job location.
`remote`	boolean	Whether the position is fully remote.
`total_comp_usd`	number, required	Total annual compensation in USD.
`base_salary_usd`	number	Annual base salary in USD.
`bonus_usd`	number	Annual bonus amount in USD.
`equity_usd`	number	Annual equity value in USD.
`signing_bonus_usd`	number	Signing bonus amount in USD.
`years_experience`	number	Total years of professional experience.
`years_at_company`	number	Years at the current company.
`education_level`	string	Highest education level (e.g., Bachelor's, Master's, PhD).
`seniority_level`	string	Seniority or career level (e.g., Senior, Staff, Principal).
`data_source`	string	Source or platform of this salary data point.
`submission_date`	string	Date this salary was submitted or reported.
`page_title`	string	Title of the source page. Tabstack auto-fills this from page metadata when left empty.
`favicon`	string	Favicon URL of the source page. Tabstack auto-fills this from page metadata when left empty.

Example

Inspect and validate the response shape.

The example below is generated from the schema to demonstrate its structure. Edit the object or paste a real response to validate it in your browser.

What it checks

Pass or fail, and the first reason why.

Whether the text parses as JSON, whether the top level is an object, and whether each field the schema requires is present at the declared type. A null is allowed anywhere. Every key you ask for is present. A field the page does not state can come back null, empty, a placeholder number, or a guessed value. Validate values, not just keys.

Example data, not a live response.

Usage

Send the schema with your source URL.

The schema travels with the request rather than living on your account, so the same call can send a trimmed version for one page and the full one for another. Every key you ask for is present. A field the page does not state can come back null, empty, a placeholder number, or a guessed value. Validate values, not just keys.

salary.tsTypeScript

import schema from './salary-data-point.json'

const res = await client.extract.json({
  url: 'https://example.com/pricing',
  json_schema: schema,
})

tabstack extract json 'https://example.com/pricing' \
  --schema @./salary-data-point.json

Adaptation

Make the schema match your application.

The schema is a starting point, not a guarantee that every source page contains every field. Three things are worth doing before you write one into your system, and the schema-authoring guide covers the rest.

Remove what you do not need

A shorter schema is a smaller response and fewer fields to handle.

Describe the ambiguous ones

A description tells Extract what to look for when a label could mean two things.

Validate before you store

Check the returned object against the schema rather than trusting it.

Related schemas

Other schemas in this category.

Same category: company hiring velocity.
Same category: executive leadership change.
Same category: job posting.
The job rather than the object: the Extract API.