n8n: Fair-code workflow automation platform with native AI capabilities

by n8n-io · MCP Server · ★ 209.8k

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

How to install n8n

npx n8n
docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 61/100

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.

aiapisautomationclidata-flowdevelopmentintegration-frameworkintegrationsipaaslow-code

Quick Facts

Stars209,789
Forks61,399
LanguageTypeScript
CategoryMCP Server
Quality Score61/100
Open Issues1233
Last Updated2026-11-08
Created2019-06-22
Platformscli, mcp, node
Est. Tokens~14k

About n8n

n8n - Secure Workflow Automation for Technical Teams n8n is a workflow automation platform that gives technical teams the flexibility of code with the speed of no-code. With 400+ integrations, native AI capabilities, and a fair-code license, n8n lets you build powerful automations while maintaining full control over your data and deployments.

Compatible Skills

These tools work well together with n8n for enhanced workflows:

  • sim — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • ToolJet — semantic(0.34)+complementary+rare_topics+similar_pop+shared_platform (61%)
  • coze-studio — semantic(0.22)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • tribe — semantic(0.16)+complementary+rare_topics+same_lang+shared_platform (49%)
  • agentic-signal — semantic(0.28)+complementary+rare_topics+same_lang+shared_platform (49%)

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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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