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pennylane

80 / 100 · Low risk

Fine to try.

By lamm-mit/scienceclaw (GitHub) · other · 251 stars · scanned at commit 894598c

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

Works with any agent that supports SKILL.md skills.

What this skill can do

Install

Paste this into your terminal. It downloads exactly the version we scanned, not whatever is newest.

if [ -e ~/.agents/skills/'pennylane' ] || [ -L ~/.agents/skills/'pennylane' ]; then echo 'A skill folder named pennylane already exists — not replacing it' >&2; false; else d=$(mktemp -d) && git clone --quiet --filter=blob:none --no-checkout 'https://github.com/lamm-mit/scienceclaw' "$d" && git -C "$d" checkout --quiet '894598cd3a90891a077c254566276bd379038bad' -- 'skills/pennylane' && mkdir -p ~/.agents/skills && cp -R "$d"/'skills/pennylane' ~/.agents/skills/'pennylane' && rm -rf "$d"; fi

Codex reads ~/.agents/skills directly. For Claude Code, Cursor, Copilot, Gemini CLI, Antigravity or OpenCode, also link it. For Claude Code: ln -s ~/.agents/skills/'pennylane' ~/.claude/skills/'pennylane' (other agents: ~/.cursor/skills, ~/.copilot/skills, ~/.gemini/skills, ~/.gemini/config/skills, ~/.config/opencode/skills). Or install through the finder skill, which does both.

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