model-pr-history-knowledge

A skill for triaging SGLang production serving incidents using a replay-first approach, helping diagnose queue growth, timeouts, wrong outputs, crashes, and distributed stalls by preserving evidence and reproducing the request path before patching.

Part of Lifeskills, a curated collection of non-coding skills for AI agents focused on business-critical communication, strategy, negotiation, and influence with decision-ready outputs.

An agent skill for AI infrastructure engineers that provides operational playbooks for torch profiler analysis, LLM serving benchmarks, and SGLang optimization. Includes skills for splitting prefill/decode profiler evidence and turning traces into kernel fusion opportunities.

Agent-ready operational playbooks for AI infrastructure engineers, covering LLM serving benchmarks across SGLang, vLLM, and TensorRT-LLM, torch-profiler trace triage, kernel optimization opportunities, SGLang patch review, and production incident replay.

An agent-ready playbook for LLM serving benchmarks, capacity planning, torch-profiler triage, SGLang/vLLM optimization, and production incident analysis. Provides structured workflows for AI infrastructure performance tuning and human code review.

Agent-ready playbooks for AI infrastructure engineers, covering LLM serving benchmarks, capacity planning, torch profiler analysis, pipeline inspection, compute simulation, and SGLang/vLLM optimization.

Agent-ready playbooks for AI infrastructure engineers covering LLM serving benchmarks, capacity planning, torch-profiler analysis, pipeline inspection, compute simulation, and SGLang/vLLM optimization with production incident triage.

An agent-ready playbook for LLM serving optimization, providing operational memory for benchmarking SGLang, vLLM, and TensorRT-LLM, analyzing serving capacity from logs, profiling at kernel level, and handling production incidents.

A collection of agent-ready playbooks for AI infrastructure engineers, covering LLM serving benchmarks, capacity planning, profiler triage, compute simulation, and SGLang/vLLM optimization with real maintainer discussion patterns.

An agent-ready playbook for AI infrastructure engineers that provides forward-pass, layer-level, and kernel-level timing analysis from torch profiler traces, part of a broader skill set covering LLM serving benchmarks, capacity planning, and SGLang/vLLM optimization.