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.
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Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install parallel-enrichmentOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install parallel-enrichmentβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/parallel-enrichment/π‘Extract and place the folder at the path above, then restart your agent.
Category
πData & AnalyticsPlatforms
What Parallel Enrichment can do for your AI workflow
Bulk data enrichment via directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 1,598+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Parallel Enrichment
Help me get started with Parallel Enrichment
Explains what Parallel Enrichment does, walks through the setup, and runs a quick demo based on your current project
Use Parallel Enrichment to bulk data enrichment via Parallel API
Invokes Parallel Enrichment with the right parameters and returns the result directly in the conversation
What can I do with Parallel Enrichment in my data & analytics workflow?
Lists the top use cases for Parallel Enrichment, with example commands for each scenario
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Parallel Enrichment extends your AI assistant with the ability to 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. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Parallel Enrichment as its underlying capability.
Parallel Enrichment works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Parallel Enrichment once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
Getting started with Parallel Enrichment takes about two minutes. Place the skill at `~/.claude/skills/parallel-enrichment/` (personal, all projects) or `.claude/skills/parallel-enrichment/` (project-specific), then restart your AI client. From that point, typing `/parallel-enrichment` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.
Parallel Enrichment has been installed 1,598 times, making it one of the more actively used skills in the Data & Analytics category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/parallel-enrichment/ for personal use (all projects), or .claude/skills/parallel-enrichment/ for project-specific use. Restart your AI client, then invoke with /parallel-enrichment or let the AI discover it automatically.
Parallel Enrichment supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Parallel Enrichment is free to install. Check the repository for licensing information.
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.
Parallel Deep Research
Deep multi-source research via Parallel API. Use when user explicitly asks for thorough research, comprehensive analysis, or investigation of a topic. For quick lookups or news, use parallel-search instead.
Research
Conduct open-ended research on a topic, building a living markdown document. Supports interactive and deep research modes.
Parallel Task Executor
Multitasking parallel execution manager. Receive multiple instructions and execute them synchronously, supporting priority scheduling, task dependencies, result collection and progress tracking. Suitable for scenarios where multiple independent tasks need to be processed concurrently.
Automate my data & analytics tasks using Parallel Enrichment
Identifies repetitive steps in your workflow and sets up Parallel Enrichment to handle them automatically
Parallel Enrichment is categorized under Data & Analytics. These skills help AI agents perform specialized tasks in this domain.