rulego-server: A lightweight dependency-free workflow automation platform

by rulego · MCP Server · ★ 56

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

Security audit verdict: SAFE · quality 69/100

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.

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.

aiai-agentsapisautomationdata-flowintergrationsipaaslow-code-platformmcpmcp-client

Quick Facts

Stars56
Forks32
LanguageJavaScript
CategoryMCP Server
Quality Score69/100
Open Issues30
Last Updated2026-04-16
Created2025-04-01
Platformscli, mcp, node
Est. Tokens~86k

About rulego-server

RuleGo-Server English | 简体中文 Official Website Contribution Guide is a lightweight, high-performance, modular, and integration-friendly automation workflow platform built on RuleGo. It can be used for automation orchestration, iPaaS (Integration Platform as a Service), API orchestration, application orchestration, AI orchestration, data processing, IoT rule engine, AI assistant, and other...

Compatible Skills

These tools work well together with rulego-server for enhanced workflows:

  • ai-agent-automation — semantic(0.31)+complementary+similar_pop+shared_platform (51%)
  • ToolJet — semantic(0.20)+complementary+same_lang+shared_platform (47%)
  • n8n — semantic(0.58)+rare_topics+shared_platform (45%)

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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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