by hike-lab · MCP Server · ★ 81
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Flagged: sudo usage. 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.
| Stars | 81 |
| Forks | 19 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 55/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-25 |
| Created | 2026-08-03 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
공공데이터 렌즈 하고 싶은 일을 말하면, 활용할 공공데이터와 선택 근거를 찾아주는 AI 탐색·판단 계층 English — Public Data Lens is a discovery-and-judgment layer over Korea's open data portal (data.go.kr): the monthly catalog (96k listings) normalized into canonical JSON-LD/DCAT with versioned deterministic quality rules, exposed to AI hosts via the Model Context Protocol.
These tools work well together with public-data-lens for enhanced workflows:
Looking for a public-data-lens alternative? If you're comparing public-data-lens with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The World's Most Comprehensive, Authoritative, and Structured Open Source Data Source Knowledge Base
MCP server for querying CKAN open data portals (package search, DataStore SQL, organizations, groups, tags)
Browser-based RDF/ontology knowledge graph editor — load RDF from files, URLs or SPARQL endpoints; author node
국토교통부 건축HUB → MCP 서버. 건축물대장·건축인허가·주택인허가 + 법정동코드 + 한 필지 종합카드·동 단위 통계·노후건축물 분석. 공용키 탑재 remote 커넥터(URL만 등록)
다이소·편의점 재고와 생활 정보를 AI에서 바로 조회하세요. 설치·API 키 없는 공개 MCP, 웹 링크 조회, CLI·Skill을 지원합니다.
Google Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to
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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: