fire-enrich: AI-powered data enrichment tool that transforms emails into rich datasets with company profiles, funding data

by firecrawl · Agent Tool · ★ 1.3k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars1,293
Forks318
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score70/100
Open Issues19
Last Updated2026-10-26
Created2025-06-06
Platformsnode
Est. Tokens~17k

About fire-enrich

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.

Compatible Skills

These tools work well together with fire-enrich for enhanced workflows:

  • OpenMetadata — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (54%)
  • agent-kit — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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