No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by firecrawl · Agent Tool · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fire-enrich safe to install? View the security audit →
Fire Enrich - AI-Powered Data Enrichment Tool Turn a simple list of emails into a rich dataset with company profiles, funding data, tech stacks, and more. Powered by Firecrawl and a multi-agent AI system. Technologies Firecrawl: Web scraping and content aggregation OpenAI: Intelligent data extraction and synthesis Next.js 15: Modern React framework with App Router Setup Required API Keys Quick Start Clone this repository Create a file with your API keys: FIRECRAWLAPIKEY=yourfirecrawlkey OPENAIAPIKEY=your
| Stars | 1,266 |
| Forks | 316 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.657511827415/100 |
| Open Issues | 17 |
| Last Updated | 2025-10-08 |
| Created | 2025-06-06 |
| Platforms | node |
| Est. Tokens | ~36k |
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fire-enrich is 🔥 AI-powered data enrichment tool that transforms emails into rich datasets with company profiles, funding data, tech stacks, and more using Firecrawl and multi-agent AI. It is categorized as a Agent Tool with 1.3k GitHub stars.
fire-enrich is primarily written in TypeScript.
You can find installation instructions and usage details in the fire-enrich GitHub repository at github.com/firecrawl/fire-enrich. The project has 1.3k stars and 316 forks, indicating an active community.
fire-enrich is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fire-enrich on Agent Skills Hub include excalidraw-diagram-skill, raptor, playwright-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: