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name: pyraformer description: "Routes Pyraformer long-range forecasting, single-step forecasting, preprocessing, synthetic data generation, and optional TVM questions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

pyraformer

Use this sub-skill when the task is about Pyraformer long-range forecasting, Pyraformer single-step forecasting, preprocessing the Pyraformer datasets, or generating synthetic input files for that route.

Route here for

• Running or explaining Pyraformer/long_range_main.py.
• Running or explaining Pyraformer/single_step_main.py.
• Preparing the Pyraformer single-step inputs from raw electricity, flow, or wind data.
• Generating synthetic.npy for the synthetic route.
• Questions about use_tvm, truncate, or other advanced Pyraformer runtime flags when the user explicitly wants the optional CUDA/TVM path.

Route away

• The graph_attention.py debug helper and other TVM-only internals are reference-only and stay out of the default runtime surface.
• Root LTSF-Linear workflows, baseline models, and FEDformer tasks belong in their sibling routes.

Read first

• references/cli-reference.md for long-range and single-step flags, dataset defaults, and model switches.
• references/data-layout.md for expected CSV, .npy, and checkpoint locations.
• references/preprocessing.md for electricity, flow, wind, and synthetic preparation details.
• references/workflows.md for the common run recipes and sweep-script summary.
• references/troubleshooting.md for dataset, mask, checkpoint, and TVM failure modes.

Skill-owned scripts

• scripts/run_pyraformer_long.py — repo-root-aware launcher for long-range runs.
• scripts/run_pyraformer_single.py — repo-root-aware launcher for single-step runs.
• scripts/prepare_pyraformer_data.py — bundled data-preparation helper for elect, flow, wind, and synthetic outputs.
• scripts/smoke_pyraformer.py — CLI help and dry-run smoke for the route.

Typical workflow

1. Confirm whether the user needs long-range forecasting, single-step forecasting, or preprocessing.
2. Check the dataset family and file layout in references/data-layout.md.
3. Use the matching wrapper or source CLI with the dataset-specific flags from references/cli-reference.md.
4. If the user asks about use_tvm, confirm they want the optional CUDA/TVM path before enabling it.
5. If a run fails, compare the error with references/troubleshooting.md before changing model settings.

Cross-links

• Use references/workflows.md for the upstream benchmark sweep summary and command patterns.
• Use scripts/smoke_pyraformer.py before asking for a heavier training or preprocessing run.
• For shared CSV layout and repo-wide missing-file or import issues, see the root skill references at ../../references/data-layout.md and ../../references/troubleshooting.md.