什么是 并行增强？

并行增强是一项专为需要扩展数据采集能力的开发人员和数据研究员设计的尖端工具。通过与 Openclaw Skills 集成，该工具允许用户将静态 CSV 或 JSON 列表转化为包含实时网页信息的增强数据集。无论您是需要查找初创公司的最新融资轮次、首席执行官的 LinkedIn 个人资料，还是特定的产品规格，此技能都能处理网页搜索和数据提取的高强度工作。

并行增强的核心价值在于其自然语言界面。用户只需描述其意图，而无需编写复杂的爬虫或手动搜索查询。该技能利用各种处理层级来平衡速度和深度，使其成为从快速联系人查询到 Openclaw Skills 生态系统内深度竞争情报分析的通用选择。

下载入口:https://github.com/openclaw/skills/tree/main/skills/normallygaussian/parallel-enrichment

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

并行增强 应用场景

• 通过将高管联系详情和社交资料追加到潜在客户列表，实现潜在客户增强自动化。
• 通过收集数千家公司的融资、总部和员工人数数据进行市场研究。
• 利用来自实时网页数据的技术规格和市场定价来增强产品数据库。
• 清理并更新 CRM 记录，确保公司名称和领导层信息是最新的。
• 通过提取行业竞争对手的市场地位和关键产品特征，扩展竞争分析规模。

1. 提供源数据，可以是内联 JSON 数组或 CSV 文件的路径。
2. 使用自然语言定义您的增强意图，准确指定您想要发现的新列或信息。
3. 可选地定义源列和增强列的描述，以提高 AI 数据映射的准确性。
4. 选择处理层级——从 lite-fast 到 ultra-fast——以匹配研究任务的复杂性。
5. 执行命令开始增强过程，该过程会提供一个监控 URL 以实时跟踪进度。
6. 获取输出 CSV，其中包含您的原始数据以及新增强的字段和每一行的成功状态。

并行增强 配置指南

要利用此技能，您必须首先从提供商处获取 API 密钥，并在本地计算机上安装 CLI。按照以下步骤开始：

# 安装 Parallel CLI 工具
curl -fsSL https://parallel.ai/install.sh | bash

# 在环境变量中设置您的 API 密钥
export PARALLEL_API_KEY=your_secret_api_key_here

安装完成后，您可以开始使用 Openclaw Skills 直接从终端处理您的数据文件。

并行增强 数据架构与分类体系

并行增强通过将信息追加到现有结构而不是覆盖它们来确保数据完整性。输出通常遵循以下模式：

对于更复杂的数据结构，用户可以定义源列元数据，以帮助 AI 有效地解释模糊的数据点。

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 r
  --data '[{"company": "Google"}, {"company": "Microsoft"}]' r
  --intent "CEO name and founding year" r
  --target output.csv

# CSV file
parallel-cli enrich run r
  --source-type csv --source input.csv r
  --target output.csv r
  --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

Processor Tiers

Examples

Inline data enrichment:

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

CSV file enrichment:

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

With explicit output columns:

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

Using AI to suggest columns:

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

# Then run with suggested columns
parallel-cli enrich run r
  --source-type csv r
  --source ./companies.csv r
  --target ./companies-analysis.csv r
  --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-.csv --intent "..."

Then spawn a sub-agent:

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

Error Handling

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