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name: spikingjelly description: "Operate the SpikingJelly package for SNN modeling, datasets, ANN-to-SNN conversion, backend performance, training scale-out, and deployment exchange." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SpikingJelly Repo Skill

Use this skill when the task is about the public spikingjelly package rather than a one-off source edit.

Start here

1. Install PyTorch first, then install spikingjelly plus only the extras needed for the selected workflow.
2. Run scripts/spikingjelly_env_report.py to confirm the installed package, torch, and optional backends; consult references/repo-provenance.md for the source anchor.
3. Choose the smallest matching sub-skill below.
4. Use the sub-skill's bundled references and smoke script before larger runs.

Route map

• sub-skills/core-snn/ — activation-based SNN modeling, step modes, reset/state, surrogate gradients, neurons, layers, recurrent wrappers, monitors, timing-based helpers, visualizing, configure, and logger.
• sub-skills/datasets/ — neuromorphic dataset loading, builders, transforms, utilities, and manual-download workflows.
• sub-skills/ann2snn/ — ANN-to-SNN conversion, calibration, transformer/Qwen2 recipes, and readout semantics.
• sub-skills/performance-and-analysis/ — CUDA/CuPy/Triton, FlexSN, precision, memory optimization, op counting, and energy/profiling.
• sub-skills/training-and-scaleout/ — model zoo, training helpers, distributed vision, and scale-out topologies.
• sub-skills/deployment-exchange/ — NIR, Lava, and Lynxi exchange and deployment.

Shared references

• references/package-overview.md
• references/repo-provenance.md
• references/troubleshooting.md

Shared script

• scripts/spikingjelly_env_report.py: lightweight environment and import report.

Safe defaults

• Prefer public package imports over source-checkout paths in runtime instructions.
• Treat optional accelerators and vendor stacks as optional unless a sub-skill explicitly verified them in the current environment.
• Keep each task in the narrowest branch that answers the user request.