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skills/magpie/.federated.json

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"source": "amd-agi-magpie",
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"repo": "AMD-AGI/Magpie",
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"ref": "main",
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"commit": "2a9263833f71755df2a93b466cdd3a9f803fc625",
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"commit": "02d9a7dfc3aedec0e3feadb7449f6c0c318621d5",
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"path": "skills/magpie",
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"license": "MIT",
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"imported_at": "2026-05-28T21:37:43Z"
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"imported_at": "2026-06-18T20:31:36Z"
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}

skills/magpie/SKILL.md

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- `--no-perf`: Skip performance profiling.
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- `-o`, `--output-dir`: Output directory (default: `./results`).
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**Config template (single kernel):** Use `kernel:` with `id`, `type`, `source_files`, `working_dir`, `testcase_command`, optional `compile_command`, `env`. See [Magpie/kernel_config.yaml.example](https://github.com/AMD-AGI/Magpie/blob/2a9263833f71755df2a93b466cdd3a9f803fc625/Magpie/kernel_config.yaml.example) and [examples/ck_gemm_add.yaml](https://github.com/AMD-AGI/Magpie/blob/2a9263833f71755df2a93b466cdd3a9f803fc625/examples/ck_gemm_add.yaml).
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**Config template (single kernel):** Use `kernel:` with `id`, `type`, `source_files`, `working_dir`, `testcase_command`, optional `compile_command`, `env`. See [Magpie/kernel_config.yaml.example](https://github.com/AMD-AGI/Magpie/blob/02d9a7dfc3aedec0e3feadb7449f6c0c318621d5/Magpie/kernel_config.yaml.example) and [examples/ck_gemm_add.yaml](https://github.com/AMD-AGI/Magpie/blob/02d9a7dfc3aedec0e3feadb7449f6c0c318621d5/examples/ck_gemm_add.yaml).
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## Compare (multiple kernels)
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- `--baseline`: Index of baseline kernel (default: 0).
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- `--no-perf`, `-o`: Same as analyze.
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Example: [examples/ck_grouped_gemm_compare.yaml](https://github.com/AMD-AGI/Magpie/blob/2a9263833f71755df2a93b466cdd3a9f803fc625/examples/ck_grouped_gemm_compare.yaml).
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Example: [examples/ck_grouped_gemm_compare.yaml](https://github.com/AMD-AGI/Magpie/blob/02d9a7dfc3aedec0e3feadb7449f6c0c318621d5/examples/ck_grouped_gemm_compare.yaml).
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## Benchmark (vLLM / SGLang)
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- `--run-mode`: `docker` (default) or `local`.
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- `--docker-image`, `--timeout`, `-o`: Override image, timeout (seconds), output dir.
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Example configs: [examples/benchmarks/benchmark_vllm_dsr1.yaml](https://github.com/AMD-AGI/Magpie/blob/2a9263833f71755df2a93b466cdd3a9f803fc625/examples/benchmarks/benchmark_vllm_dsr1.yaml), [docs/benchmark.md](https://github.com/AMD-AGI/Magpie/blob/2a9263833f71755df2a93b466cdd3a9f803fc625/docs/benchmark.md).
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Example configs: [examples/benchmarks/benchmark_vllm_dsr1.yaml](https://github.com/AMD-AGI/Magpie/blob/02d9a7dfc3aedec0e3feadb7449f6c0c318621d5/examples/benchmarks/benchmark_vllm_dsr1.yaml), [docs/how-to/benchmark.md](https://github.com/AMD-AGI/Magpie/blob/02d9a7dfc3aedec0e3feadb7449f6c0c318621d5/docs/how-to/benchmark.md).
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## Gap analysis (standalone)
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When the user needs a kernel config file:
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1. Emit YAML matching the structure in [Magpie/kernel_config.yaml.example](https://github.com/AMD-AGI/Magpie/blob/2a9263833f71755df2a93b466cdd3a9f803fc625/Magpie/kernel_config.yaml.example): `kernel:` with `id`, `type` (hip|cuda|pytorch), `source_files`, `working_dir`, `testcase_command`, and optionally `compile_command`, `env`.
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1. Emit YAML matching the structure in [Magpie/kernel_config.yaml.example](https://github.com/AMD-AGI/Magpie/blob/02d9a7dfc3aedec0e3feadb7449f6c0c318621d5/Magpie/kernel_config.yaml.example): `kernel:` with `id`, `type` (hip|cuda|pytorch), `source_files`, `working_dir`, `testcase_command`, and optionally `compile_command`, `env`.
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2. Write the file to the user's requested path (e.g. `kernel_config.yaml`).
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3. Run: `magpie analyze --kernel-config <that_file>`.
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skills/magpie/reference.md

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## Benchmark config YAML
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Top-level key `benchmark:` with `framework`, `model`, `precision`, `envs`, `profiler`, `gap_analysis`, `timeout_seconds`, etc. See `examples/benchmarks/benchmark_vllm_dsr1.yaml` and `docs/benchmark.md`.
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Top-level key `benchmark:` with `framework`, `model`, `precision`, `envs`, `profiler`, `gap_analysis`, `timeout_seconds`, etc. See `examples/benchmarks/benchmark_vllm_dsr1.yaml` and `docs/how-to/benchmark.md`.

skills/magpie/skill-card.md

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## Description
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Perform GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie (HIP/CUDA/PyTorch kernels, vLLM/SGLang benchmarks, TraceLens, and torch-trace gap analysis).
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Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces.
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## Owner
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