@wbavon

perf-test-flagos runs accuracy (when available) and performance benchmarks against a served model across 5 workload profiles, collecting throughput, latency, ttft, and tpot metrics. currently accuracy testing is a placeholder.

model-migrate-flagos guides backporting vllm models to the flagos plugin pinned at v0.13.0, with argument parsing, upstream reference loading, step-by-step execution, and e2e verification against ground truth.

Full FlagRelease pipeline orchestrator. Runs the complete LLM deployment, verification, and benchmarking pipeline for multi-chip GPU backends. Executes: inst...

Automatically detect GPU vendor, find appropriate PyTorch container image, launch with correct mounts, and validate GPU functionality. Supports NVIDIA, Ascen...

Install the 5-package multi-chip software stack (vLLM, FlagTree, FlagGems, FlagCX, vllm-plugin-FL) inside a GPU container. Handles network mirror detection,...

Verify the serving stack with a user-specified target model. Runs twice: first with FlagGems/FlagCX disabled (isolate model-specific errors), then with full...

Unified GPU kernel operator generation and optimization skill. Automatically detects the target repository type (FlagGems, vLLM, or general Python/Triton) an...

Create new skills, modify existing skills, and validate skill quality for the FlagOS skills repository. Use this skill whenever someone wants to create a ski...

Install and configure vLLM-Plugin-FL for multiple hardware backends including NVIDIA, Ascend and etc. Use when setting up vllm-plugin-fl, configuring the env...

Self-contained orchestration skill for writing high-performance TLE kernels and shipping TLE feature changes with reproducible validation. Use when the user...