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已展示 40 / 46 个已收集 Skill。
Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning. Use this skill whenever a downstream skill needs confirmed hardware and toolchain facts, when the user mentions their setup, when…
原文语言：英语
Validate workspace shape, model paths, output directories, and repo structure before downstream Quark skills proceed. Use this skill whenever a skill needs confirmed file paths, when the user provides a model path or output directory, when you need to…
原文语言：英语
Diagnose failed Quark ONNX installation, calibration, quantization, custom-op compilation, or export attempts. Use when the user reports an error, stack trace, invalid artifact, missing dependency, ORT execution-provider mismatch, silent CPU fallback, OOM…
原文语言：英语
Install or verify the correct ONNX Runtime build (and the `onnx` package) for a user's accelerator backend before Quark's ONNX-to-ONNX flow. Use when the user needs ONNX Runtime set up, reports onnxruntime version conflicts, CPU vs GPU variant mix-ups (only…
原文语言：英语
Inspect a target ONNX model and prepare metadata for Quark ONNX PTQ planning. Use when the user needs `.onnx` path validation, opset / IR version detection, input/output shape and dtype discovery, op-type histogram, quantizable-op counting, deployment-target…
原文语言：英语
Build a Quark ONNX PTQ quantization plan from `model_analysis.json` and user intent. Use when the user needs preset selection (XINT8 / A8W8 / A16W8 / BF16 / BFP16 / MX* / MXFP* …), calibration method choice (MinMax / Entropy / Percentile / Distribution /…
原文语言：英语
Validate Quark ONNX quantization output using four lightweight checks: auxiliary file copy alignment, expected non-quantized initializer MD5 byte-identity (inline `raw_data` + external-data byte ranges), model metadata equality after stripping…
原文语言：英语
Route Quark ONNX user goals to the correct atomic skill. Use when a user describes an ONNX quantization task in plain language — such as "install onnxruntime", "analyze my .onnx model", "choose a preset for my YOLO model", "plan ONNX PTQ", "quantize this…
原文语言：英语
Install or verify the AMD Quark package and its dependencies. Use when the user needs Quark package installation, dependency setup, or post-install verification — after PyTorch is already set up. Trigger for "install Quark", "set up Quark", "pip install…
原文语言：英语
Diagnose failed Quark installation, PTQ execution, script generation, or export attempts. Use when the user reports an error, stack trace, invalid artifact, missing dependency, CUDA OOM, version mismatch, or unexpected PTQ results. Trigger for "Quark error",…
原文语言：英语
Prepare export and downstream evaluation handoff for a planned or completed Quark PTQ run. Use when the user wants to export a quantized model to HuggingFace SafeTensors, ONNX, or GGUF format, package for deployment, or set up post-quantization evaluation.…
原文语言：英语
Low-memory file2file quantization for very large safetensors LLMs that cannot be loaded whole. Use when the user wants to run file2file quantization, adapt a new safetensors checkpoint without loading the full model, register an external LLMTemplate, inspect…
原文语言：英语
Install or verify the correct PyTorch build for a user's accelerator backend before Quark installation. Use when the user needs PyTorch set up, reports torch version conflicts, CUDA/ROCm package mismatches, or when torch.cuda.is_available() returns False.…
原文语言：英语
End-to-end LLM accuracy evaluation on AMD ROCm (ROCm-only) — container setup, vLLM/SGLang/ATOM serving, lm-eval / lighteval / evalscope benchmarks. Use when the user wants to evaluate, benchmark, or compare an LLM's accuracy. Trigger for "evaluate this…
原文语言：英语
Inspect a target model and prepare metadata for Quark PTQ planning. Use when the user needs model path validation, architecture detection, quantization target discovery, layer counting, risk assessment, or transformer compatibility checks before planning PTQ.…
原文语言：英语
Build a Quark Torch LLM PTQ quantization plan from model analysis and user intent. Use when the user needs quantization scheme recommendations, exclusion lists, algorithm selection, KV cache decisions, per-layer overrides, or a draft quant_plan. Trigger for…
原文语言：英语
Validate Quark quantization output using four lightweight checks: auxiliary file copy alignment, excluded tensor MD5 byte-identity, config.json deep comparison after stripping quantization keys, and safetensors header pattern/dtype summaries. Intended for…
原文语言：英语
Route Quark user goals to the correct atomic skill or workflow. Use when a user describes a Quark task in plain language — such as "install Quark", "quantize a model", "analyze my model", "build a PTQ plan", "export the quantized model", "debug a failed run",…
原文语言：英语
End-to-end ONNX PTQ workflow for AMD Quark — from a `.onnx` file (and calibration data) to a quantized `.onnx` output. Use when the user wants a complete ONNX-to-ONNX PTQ pipeline: model intake, quantization planning, calibration-script generation, manifest,…
原文语言：英语
Torch LLM PTQ workflow for AMD Quark — from model selection to quantized output. Use when the user wants a complete PTQ pipeline: model inspection, quantization planning, script generation, and optional execution. Stops at the quantized output. Trigger for…
原文语言：英语
L3 recipe that runs `quark.onnx.AutoSearchPro` end-to-end on a user `.onnx` model: intake → preset selection (or custom search space) → calibration / eval data reader → standalone autosearch script generation → confirmed execution → best_params reporting.…
原文语言：英语
L3 recipe that runs a Torch LLM PTQ end-to-end for AMD Quark — for PyTorch / HuggingFace transformers models (safetensors input): quantize → validate → evaluate. Phase 1 delegates the full PTQ path (model intake → quantization planning → manifest generation →…
原文语言：英语
Compare ONNX skill contracts and guidance against current Quark ONNX documentation and source entry points. Use when maintainers need to verify that ONNX install docs, custom-op registry, QConfig fields, preset and calibration lists, AutoSearchPro presets,…
原文语言：英语
Manually verify that the Quark ONNX skill family behaves correctly across the four contract categories (routing, planning, artifact, recovery). Use when maintainers need to confirm that ONNX routing, planning, artifact generation, or error recovery skills…
原文语言：英语
Detect upstream Quark ONNX changes that affect the ONNX skill family and classify required updates. Use when Quark ONNX docs, custom-op registry, quantization config presets, calibration methods, AutoSearchPro presets, ONNX Runtime install matrix, or source…
原文语言：英语
Author or restructure a Quark Agent Skill so it conforms to this project's template, contracts, and layer rules. Use when a maintainer says "create a new skill", "add a skill for X", "scaffold a skill", "draft a SKILL.md", or when an existing skill needs a…
原文语言：英语
Compare skill contracts and guidance against current Quark documentation and source entry points. Use when maintainers need to verify that installation docs, artifact schemas, PTQ planning assumptions, CLI flags, or supported model lists still match upstream…
原文语言：英语
Manually verify that the Quark Agent Skills system behaves correctly across the four contract categories (routing, planning, artifact, recovery). Use when maintainers need to confirm that routing, planning, artifact generation, or error recovery skills still…
原文语言：英语
Detect upstream Quark changes that affect the skill system and classify required updates. Use when Quark docs, CLI flags, quantization templates, model support, or source behavior may have drifted from the skill contracts. Trigger for "check if skills are up…
原文语言：英语
Collect and normalize environment facts (OS, Python, GPU, CUDA/ROCm, container state) before Quark installation or PTQ planning. Trigger for "check my environment", "what GPU do I have", "is my setup ready for Quark", or when any accelerator-related…
原文语言：英语
Install or verify the AMD Quark package and its dependencies. Trigger for "install Quark", "set up Quark", "pip install amd-quark", dependency errors, import failures for quark modules, ModuleNotFoundError for quark, or missing C++ compiler errors. For…
原文语言：英语
End-to-end Quark ONNX AutoSearchPro recipe — drives `quark.onnx.AutoSearchPro` (Optuna-based hyperparameter search) on a `.onnx` model to find the best quantization config (activation/weight spec, calibration method, CLE, AdaRound / AdaQuant, FastFinetune…
原文语言：英语
Diagnose failed Quark ONNX installation, calibration, quantization, custom-op compilation, or export attempts. Use when the user reports an error, stack trace, invalid artifact, missing dependency, ORT execution-provider mismatch, silent CPU fallback, OOM…
原文语言：英语
Install or verify the correct ONNX Runtime build (and the matching `onnx` package) for a user's accelerator backend before Quark ONNX-flow usage. Trigger for "install onnxruntime", "pip install onnxruntime", "set up onnxruntime for ROCm", "set up onnxruntime…
原文语言：英语
Inspect a target ONNX model and prepare metadata for Quark ONNX PTQ planning. Use for `.onnx` path validation, opset / IR version detection, input-output shape and dtype discovery, op-type histogram, quantizable-op counting, deployment-target compatibility…
原文语言：英语
End-to-end ONNX PTQ workflow for AMD Quark — for `.onnx` input models (with optional sibling `.onnx_data` external-weights file). Use when the user wants a complete ONNX-to-ONNX pipeline: model intake, quantization planning, calibration-script generation,…
原文语言：英语
Validate Quark ONNX quantization output using four lightweight checks: auxiliary file copy alignment, expected non-quantized initializer MD5 byte-identity (inline `raw_data` + external-data byte ranges), model metadata equality after stripping…
原文语言：英语
Diagnose failed Quark Torch PTQ installation, execution, script generation, or export attempts. Use when the user reports a torch-side error, stack trace, invalid artifact, missing dependency, CUDA OOM, version mismatch, or unexpected PTQ results. Trigger for…
原文语言：英语
Prepare export and downstream evaluation handoff for a planned or completed Quark Torch PTQ run. Input is a PyTorch / HuggingFace transformers model; output formats include HF safetensors, GGUF, and ONNX. Trigger for "export model", "save quantized model",…
原文语言：英语
Low-memory file2file quantization for very large safetensors LLMs that cannot be loaded whole. Use when the user wants to run file2file quantization, adapt a new safetensors checkpoint without loading the full model, register an external LLMTemplate, inspect…
原文语言：英语