harshanandak

parallel-data-enrichment

"Structured company and entity data enrichment using Parallel AI Task API with core/base processors. Returns typed JSON output. No binary install — requires PARALLEL_API_KEY in .env.local."

harshanandak 4 Updated 6mo ago
GitHub

Install

npx skillscat add harshanandak/forge/parallel-data-enrichment

Install via the SkillsCat registry.

About this skill

This skill enriches company or entity data into typed JSON using the Parallel AI Task API, accepting custom output schemas to return structured fields like founding year, headquarters, and key products with citations. It addresses the need for reliable, schema-validated entity data without manual research, choosing between a faster, cheaper `base` processor or a more thorough `core` processor based on accuracy needs. Developers should use it when building applications that require up-to-date company information via API calls using curl and a PARALLEL_API_KEY.

SKILL.md

Parallel Data Enrichment

Enrich company or entity data into structured JSON using the Task API. Use core (1-5 min, $0.025) or base (15-100s, $0.01) for structured output.

CLI alternative (recommended): Install parallel-cli for official skill:
npx skills add parallel-web/parallel-agent-skills --skill parallel-data-enrichment

Setup

API_KEY=$(grep "^PARALLEL_API_KEY=" .env.local | cut -d= -f2)

Create Enrichment Task

curl -s -X POST "https://api.parallel.ai/v1beta/tasks/runs" \
  -H "x-api-key: $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "OpenAI",
    "processor": "core",
    "output_schema": {
      "type": "object",
      "properties": {
        "name": {"type": "string"},
        "founded_year": {"type": "integer"},
        "headquarters": {"type": "string"},
        "employee_count": {"type": "integer"},
        "key_products": {"type": "array", "items": {"type": "string"}}
      }
    }
  }'

Response: {"id": "task_abc123", "status": "queued"}

Check Result

curl -s "https://api.parallel.ai/v1beta/tasks/runs/task_abc123" \
  -H "x-api-key: $API_KEY"
{
  "id": "task_abc123",
  "status": "completed",
  "result": {
    "content": {
      "name": "OpenAI",
      "founded_year": 2015,
      "headquarters": "San Francisco, CA",
      "employee_count": 770,
      "key_products": ["ChatGPT", "GPT-4", "DALL-E", "Whisper"]
    },
    "basis": {
      "citations": [{"url": "...", "excerpt": "..."}]
    }
  }
}

Processors

Processor Speed Cost Use For
base 15-100s $0.01/task Quick lookups, simple data
core 1-5 min $0.025/task Enrichment, verification, structured data

Tips for output_schema

  • Keep schemas simple — fewer fields = more reliable
  • Use "type": "string" broadly; avoid strict enums
  • Omit optional fields from the schema

When to Use

  • Company or person data enrichment
  • Structured data extraction with typed schemas
  • Lead qualification, CRM enrichment, research

For narrative reports, use parallel-deep-research. For web search, use parallel-web-search.