by YanpengQi7 · MCP Server · ★ 65
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AI Reliability Copilot Turn incident chaos into a structured 9-section response in seconds. Paste a production incident — logs, metrics, on-call notes. The copilot streams back a structured analysis: severity, ranked root-cause hypotheses with evidence, a copy-pasteable investigation checklist, a mitigation plan with rollback steps, customer-facing impact, postmortem skeleton, and prioritized follow-ups. But the real story isn't the prompt. It's the eval pipeline — a 5-dimension rubric, a 5-scenario regression suite, and an LLM-as-judge that scores every change, so prompt iteration is measured instead of vibes-based. Live demo: ai-reliability-copilot.vercel.app 📖 Usage guide (中文): USAGE.md — how to actually use it, end-to-end Methodology deep-dive: EVALUATION.md Architecture ┌───────────────────┐ ┌───────────────────┐ ┌──────────────────┐ │ Browser (RSC) │◀────▶│ Next.js 16 App │◀────▶│ DeepSeek (AI SDK)│ │ experimental │ │ Router on Vercel │ │ generate/stream │ │ useObject hook │ │ (Fluid Compute) │ │ Object │ └───────────────────┘ └─────────┬─────────┘ └──────────────────┘ │ ▼ ┌───────────────────┐
| Stars | 65 |
| Forks | 0 |
| Language | TypeScript |
| Category | MCP Server |
| Quality Score | 59.890750904296/100 |
| Last Updated | 2026-06-12 |
| Created | 2026-05-25 |
| Platforms | mcp, node |
| Est. Tokens | ~48k |
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ai-reliability-copilot is Turn a production incident into a structured 9-section LLM response (severity, root cause, mitigation, postmortem). Ships with a 5-scenario regression suite + LLM-as-judge eval pipeline.. It is categorized as a MCP Server with 65 GitHub stars.
ai-reliability-copilot is primarily written in TypeScript. It covers topics such as ai-sdk, deepseek, incident-response.
You can find installation instructions and usage details in the ai-reliability-copilot GitHub repository at github.com/YanpengQi7/ai-reliability-copilot. The project has 65 stars and 0 forks, indicating an active community.
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