一键导入
compatibility-triage
Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | compatibility-triage |
| description | Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system. |
Prevent agents from making unsupported assumptions about ROCm-DS compatibility.
Collect environment facts using rocminfo, hipcc --version, Python version, and OS release.
Map the requested workflow to one or more ROCm-DS components.
Compare the environment to official support statements and tested-GPU notes.
Classify the request:
Stop unsafe automation when the mismatch is material.
Produce a short report with exact blockers and realistic next moves.
Develop and extend gfxGRAPH internals — add gap bridges, fix HIP/ROCm issues, debug graph capture failures, and understand the architecture. Covers BridgedCUDAGraph, ShapeBucketPool, ConditionalGraph, native bridge, and the monkey-patch system. USE FOR: extend gfxGRAPH, add new gap bridge, fix gfxGRAPH bug, understand gfxGRAPH architecture, modify BridgedCUDAGraph, add gfxGRAPH feature, debug graph capture internals, improve gfxGRAPH performance, gfxGRAPH C++ native bridge, write gfxGRAPH tests. DO NOT USE FOR: just using gfxGRAPH in a project (use gfxgraph-integration), general ROCm issues.
Benchmark gfxGRAPH internals, run the public benchmark suite, and compare performance between Python and Rust.
Guide and resources for deployment and development using the ROCm/AMD ATOM (AiTer Optimized Model) inference backend.
Validates and benchmarks ROCm-DS component ports against their CPU baselines (e.g., pandas vs hipDF). Use this to generate parity tests, verify correctness, and measure the performance speedups of GPU-accelerated code.
Migrates pandas-like workflows to hipDF and cudf.pandas style acceleration, auditing for unsupported features.
Leverages GPU acceleration to process and analyze complex graph structures using hipGRAPH. Note: hipGRAPH is early access.