
from ai-infra-auto-driven-skills699
Analyze LLM torch profiler traces to identify layer-level bottlenecks, kernel flows, and timing gaps in forward passes.
This skill provides high-resolution analysis of LLM execution pipelines by processing Chrome-trace JSON files from torch profiler. It transforms coarse trace data into actionable timing tables, enabling developers to pinpoint exactly which layers or kernels are causing performance degradation.
Activate this skill when a general profiler summary is too vague. It is especially useful for models with complex architectures (like DeepSeek-V4 NSA) where alternating layer types or specific anchor kernels are needed to delineate boundaries between attention and FFN blocks.
layer_timeline_analyzer.py for steady-state window detection, layer_kernel_breakdown.py for deep-dive compute flow analysis, and perfetto_time_mapper.py for UI navigation.Designed for agents with shell execution capabilities and filesystem access to model configs and trace files, such as Codex, Claude Code, or other ACP-enabled harnesses.
This skill has not been reviewed by our automated audit pipeline yet.
vLLM Qwen3 Coder Optimization
Guided, PR-backed manual for auditing, debugging, and extending the Qwen3 Coder tool parser in vLLM—focuses on schema edge cases, tool-parser regressions, and v
vLLM Qwen3.6 Optimization
Guidance and PR-driven validation steps for optimizing and documenting Qwen3.6 support in vLLM; use when auditing or implementing model-specific changes.