parallel-enrichment – OpenClaw Skill

---

name: parallel-enrichment description: "Bulk data enrichment via Parallel API. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data." homepage: https://parallel.ai

Parallel Enrichment

Bulk data enrichment that adds web-sourced fields to lists of companies, people, or products. Describe what you want in natural language.

When to Use

Trigger this skill when the user asks for:

• "enrich this list with...", "add CEO names to...", "find funding for these companies..."
• "look up contact info for...", "get LinkedIn profiles for..."
• Bulk data operations on CSV files or lists
• Adding web-sourced columns to existing datasets
• Lead enrichment, company research, product comparison

Quick Start

# Inline data
parallel-cli enrich run \
  --data '[{"company": "Google"}, {"company": "Microsoft"}]' \
  --intent "CEO name and founding year" \
  --target output.csv

# CSV file
parallel-cli enrich run \
  --source-type csv --source input.csv \
  --target output.csv \
  --intent "CEO name and founding year"

CLI Reference

Basic Usage

parallel-cli enrich run [options]

Note: There is no --json flag for enrich. Results are written to the target file.

Common Flags

Flag	Description
--data "<json>"	Inline JSON array of records
--source-type csv	Source file type
--source <path>	Input CSV file path
--target <path>	Output CSV file path
--source-columns "<json>"	Describe input columns
--enriched-columns "<json>"	Specify output columns
--intent "<description>"	Natural language description of what to find
--processor <tier>	Processing tier (see table below)

Processor Tiers

Processor	Use Case
lite-fast	Simple lookups
base-fast	Basic enrichment
core-fast	Standard enrichment
pro-fast	Deep enrichment (default)
ultra-fast	Complex multi-source enrichment

Examples

Inline data enrichment:

parallel-cli enrich run \
  --data '[{"company": "Stripe"}, {"company": "Square"}, {"company": "Adyen"}]' \
  --intent "CEO name, headquarters city, and latest funding round" \
  --target ./companies-enriched.csv

CSV file enrichment:

parallel-cli enrich run \
  --source-type csv \
  --source ./leads.csv \
  --target ./leads-enriched.csv \
  --source-columns '[{"name": "company_name", "description": "Company name"}]' \
  --intent "Find CEO name, company size, and LinkedIn company page URL"

With explicit output columns:

parallel-cli enrich run \
  --data '[{"name": "Sam Altman"}, {"name": "Satya Nadella"}]' \
  --source-columns '[{"name": "name", "description": "Person full name"}]' \
  --enriched-columns '[
    {"name": "current_company", "description": "Current company/employer"},
    {"name": "title", "description": "Current job title"},
    {"name": "twitter", "description": "Twitter/X handle"}
  ]' \
  --target ./people-enriched.csv

Using AI to suggest columns:

# First, get AI suggestions
parallel-cli enrich suggest \
  --source-type csv \
  --source ./companies.csv \
  --intent "competitor analysis data"

# Then run with suggested columns
parallel-cli enrich run \
  --source-type csv \
  --source ./companies.csv \
  --target ./companies-analysis.csv \
  --intent "competitor analysis: market position, key products, recent news"

Best-Practice Prompting

Intent Description

Write 1-2 sentences describing:

• What specific fields you want to add
• Context about the data (B2B companies, tech startups, etc.)
• Any constraints (recent data, specific sources)

Good:

--intent "Find CEO name, total funding raised, and number of employees for B2B SaaS companies"

Poor:

--intent "Find stuff about these companies"

Source Column Descriptions

When using --source-columns, provide context:

[
  {"name": "company", "description": "Company name, may include Inc/LLC suffix"},
  {"name": "website", "description": "Company website URL, may be partial"}
]

Response Format

The CLI outputs:

• A monitoring URL to track progress
• Status updates as rows are processed
• Final output written to target CSV

The target CSV contains:

• All original columns from the source
• New enriched columns as specified
• A _parallel_status column indicating success/failure per row

Output Handling

After enrichment completes:

1. Report the number of rows enriched
2. Preview the first few rows: head -6 output.csv
3. Share the full path to the output file
4. Note any rows that failed enrichment

Configuration File

For complex enrichments, use a YAML config:

# enrich-config.yaml
source:
  type: csv
  path: ./input.csv
  columns:
    - name: company_name
      description: "Company legal name"
    - name: website
      description: "Company website URL"

target:
  type: csv
  path: ./output.csv

enriched_columns:
  - name: ceo_name
    description: "Current CEO full name"
  - name: employee_count
    description: "Approximate number of employees"
  - name: funding_total
    description: "Total funding raised in USD"

processor: pro-fast

Then run:

parallel-cli enrich run enrich-config.yaml

Running Out of Context?

For large enrichments, save results and use sessions_spawn:

parallel-cli enrich run --source-type csv --source input.csv --target /tmp/enriched-<topic>.csv --intent "..."

Then spawn a sub-agent:

{
  "tool": "sessions_spawn",
  "task": "Read /tmp/enriched-<topic>.csv and summarize the results. Report row count, success rate, and preview first 5 rows.",
  "label": "enrich-summary"
}

Error Handling

Exit Code	Meaning
0	Success
1	Unexpected error (network, parse)
2	Invalid arguments
3	API error (non-2xx)

Common issues:

• Row failures: Check _parallel_status column in output
• Timeout: Use smaller batches or lower processor tier
• Rate limits: Add delays between large enrichments

Prerequisites

1. Get an API key at parallel.ai
2. Install the CLI:

curl -fsSL https://parallel.ai/install.sh | bash
export PARALLEL_API_KEY=your-key

References

• API Docs
• Enrichment API Reference