Example Flows - Jobo Enterprise

Filter Remote Jobs & Remap Fields

Sync only remote jobs, reshaped to a flat custom schema. No AI, no setup, no cost — this is the Filter Remote Jobs & Remap Fields template in the picker.

Flow

Configuration

Filter Node — mode include, logic all:

Path	Operator	Value
`is_remote`	`equals`	`true`

Field Mapper Node — mode mapped_only:

Source	Target
`id`	`id`
`title`	`job_title`
`company.name`	`company_name`
`listing_url`	`url`
`locations[0].country`	`country`
`date_posted`	`posted_at`

Result

Non-remote jobs are dropped (counted as filtered in the push log, not written). Remote jobs arrive at your destination as flat documents in your own naming scheme. Everything runs in sub-millisecond time per job.

Extract Requirements & Responsibilities

Parse job descriptions to extract structured requirements and responsibilities using AI, then merge back with the original data.

Flow

Configuration

AI Node:

Model: any OpenRouter model — the built-in templates default to liquid/lfm2-8b-a1b
System Prompt: “You are a job data extraction assistant. Extract requirements (must-have and preferred) and key responsibilities from the job description. Always respond with valid JSON.”
User Prompt: “Extract requirements and responsibilities from this job posting:\n\n{{input}}”
Output Schema:

{
  "type": "object",
  "properties": {
    "requirements": {
      "type": "object",
      "properties": {
        "must_have": { "type": "array", "items": { "type": "string" } },
        "preferred": { "type": "array", "items": { "type": "string" } }
      }
    },
    "responsibilities": { "type": "array", "items": { "type": "string" } }
  }
}

JSON Merge Node:

Strategy: deep
Conflict Resolution: patch wins

Result

The original job object is enriched with structured requirements and responsibilities fields extracted by AI. All original fields (title, company, locations, etc.) are preserved through the merge.

This costs one LLM call per synced job on your OpenRouter key. Consider adding a Filter node after Input so only jobs you actually want are processed.

Generate SEO Metadata

Generate SEO-optimized titles, meta descriptions, and keywords for job postings. Uses a Liquid node to prepare a focused context first, reducing AI token usage.

Flow

Configuration

Liquid Node — prepares a concise context for the AI:

{
  "title": "{{ data.title }}",
  "company": "{{ data.company.name }}",
  "location": "{{ data.locations[0].city }}, {{ data.locations[0].country }}",
  "is_remote": {{ data.is_remote }},
  "description_preview": "{{ data.description | strip_html | truncatewords: 100 }}"
}

AI Node:

System Prompt: “Generate SEO metadata for a job posting. Respond with valid JSON only.”
User Prompt: “Generate SEO-optimized metadata for this job:\n\n{{input}}”
Output Schema:

{
  "type": "object",
  "properties": {
    "seo_title": { "type": "string", "description": "Max 60 characters" },
    "meta_description": {
      "type": "string",
      "description": "Max 155 characters"
    },
    "keywords": {
      "type": "array",
      "items": { "type": "string" },
      "description": "5-10 keywords"
    }
  }
}

JSON Merge Node:

Strategy: deep
Conflict Resolution: patch wins

Result

The original job object gains SEO fields (seo_title, meta_description, keywords) while preserving all original data. Ready for search engine indexing.

Schema Mapping with Liquid

When the Field Mapper isn’t expressive enough — computed fields, conditionals, string formatting — use a Liquid node for schema mapping. Still no AI: sub-millisecond and fully deterministic.

Flow

Configuration

Liquid Node — remaps Jobo’s schema to your internal schema with a computed field:

{
  "external_id": "{{ data.id }}",
  "job_title": "{{ data.title }}",
  "employer_name": "{{ data.company.name }}",
  "city": "{{ data.locations[0].city }}",
  "country": "{{ data.locations[0].country }}",
  "remote_allowed": {{ data.is_remote }},
  "apply_link": "{{ data.apply_url }}",
  "date_posted": "{{ data.date_posted | date: '%Y-%m-%d' }}",
  "compensation_range": {% if data.compensation %}"{{ data.compensation.min }}-{{ data.compensation.max }} {{ data.compensation.currency }}"{% else %}null{% endif %},
  "skills": [{% for skill in data.all_skills %}"{{ skill }}"{% unless forloop.last %}, {% endunless %}{% endfor %}]
}

Result

Each job is remapped from Jobo’s standard schema to your custom schema, including fields the Field Mapper can’t compute (the joined compensation range, the conditional null).

If you only need to rename and move fields, prefer the Field Mapper node — same result, no template syntax to maintain.

When to Use Each Pattern

Use Case	Pattern	Nodes Used	Cost per job
Sync a subset of jobs	Filter → Map	Filter + Field Mapper	Free
Field renaming / restructuring	Field Mapper or Liquid	Field Mapper / Liquid	Free
Computed / conditional fields	Schema Mapping with Liquid	Liquid only	Free
AI enrichment (skills, classification)	Branch & Merge	AI + JSON Merge	1 LLM call
SEO generation with context prep	Prepare → Transform → Merge	Liquid + AI + JSON Merge	1 LLM call

​Filter Remote Jobs & Remap Fields

​Flow

​Configuration

​Result

​Extract Requirements & Responsibilities

​Flow

​Configuration

​Result

​Generate SEO Metadata

​Flow

​Configuration

​Result

​Schema Mapping with Liquid

​Flow

​Configuration

​Result

​When to Use Each Pattern

Filter Remote Jobs & Remap Fields

Flow

Configuration

Result

Extract Requirements & Responsibilities

Flow

Configuration

Result

Generate SEO Metadata

Flow

Configuration

Result

Schema Mapping with Liquid

Flow

Configuration

Result

When to Use Each Pattern