by ccfos · MCP Server · ★ 13.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/ccfos/nightingale.git
Taken from the project's README on GitHub. Check the security audit before running it.
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.
| Stars | 13,462 |
| Forks | 1,798 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65/100 |
| Open Issues | 243 |
| Last Updated | 2026-11-06 |
| Created | 2020-03-03 |
| Platforms | go, mcp |
| Est. Tokens | ~18k |
Open-source alerting for your existing stack Quick start · Documentation · Releases · Community English | 中文 Nightingale brings alert rules, event processing, and notification routing into one place. Connect Prometheus, VictoriaMetrics, Elasticsearch, Loki, ClickH
Looking for a nightingale alternative? If you're comparing nightingale 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.
SigNoz is an open-source, OpenTelemetry-native observability platform for your team and their AI agents. Get l
Open-source status page, incident response and uptime monitoring
SRE Agent - CNCF Sandbox Project
Laminar - open-source observability platform purpose-built for AI agents. YC S24.
Yet another WebUI for Nginx
AI observability platform for production LLM and agent systems.
Explore other popular mcp server tools:
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: