@wbavon

vllm-plugin-fl-setup-flagos installs and configures vllm-plugin-fl across multiple hardware backends (nvidia, ascend, moore threads, iluvatar) with backend-specific environment setup, dependency verification, and inference validation.

install-stack-flagos deploys a 5-package gpu software stack (vllm, flagtree, flaggems, flagcx, vllm-plugin-fl) into a running container. covers network detection, dependency ordering, per-package validation, and structured error reporting with explicit gate logic.

gpu-container-setup-flagos detects gpu vendor, retrieves pytorch container images from vendor hubs with fallback chains, mounts devices and data volumes, and validates pytorch functionality across nvidia, amd, ascend, metax, and iluvatar gpus.

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...

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...

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