"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."
Install
npx skillscat add harshanandak/forge/parallel-data-enrichment Install via the SkillsCat registry.
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.
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, $0.01) for structured output.
CLI alternative (recommended): Install
parallel-clifor official skill:npx skills add parallel-web/parallel-agent-skills --skill parallel-data-enrichment
Setup
API_KEY=$(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.