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.
Best use case
parallel-data-enrichment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using parallel-data-enrichment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/parallel-data-enrichment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How parallel-data-enrichment Compares
| Feature / Agent | parallel-data-enrichment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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, $0.01) for structured output.
> **CLI alternative (recommended)**: Install `parallel-cli` for official skill:
> `npx skills add parallel-web/parallel-agent-skills --skill parallel-data-enrichment`
## Setup
```bash
API_KEY=$(grep "^PARALLEL_API_KEY=" .env.local | cut -d= -f2)
```
## Create Enrichment Task
```bash
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
```bash
curl -s "https://api.parallel.ai/v1beta/tasks/runs/task_abc123" \
-H "x-api-key: $API_KEY"
```
```json
{
"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
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