UnifiedModel: The semantic layer that makes enterprise data understandable to AI agents

by alibaba · MCP Server · ★ 437

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

Security audit verdict: SAFE · quality 65/100

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.

ai-agentaiopscmdbdata-modelingknowledge-graphmcpobject-graphobservabilityopentelemetrysemantic-layer

Quick Facts

Stars437
Forks69
LanguagePython
CategoryMCP Server
Quality Score65/100
Open Issues22
Last Updated2026-10-12
Created2026-05-06
Platformsmcp, python
Est. Tokens~15k

About UnifiedModel

UModel 中文版本:READMECN.md UModel (Unified Model) is a vendor-neutral semantic runtime for enterprise AI, data governance, and operational intelligence. It turns fragmented schemas, entities, business objects, telemetry links, and topology relations into workspace-scoped graph context that humans, systems, and AI agents can understand and use through one local service.

Compatible Skills

These tools work well together with UnifiedModel for enhanced workflows:

  • databao-context-engine — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • llm-movieagent — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)

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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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