Backend-extension skill for running PennyLane workflows on Qiskit simulators and IBM-compatible paths.
EnglishCore implementation skill for PennyLane-first hybrid quantum neural network models.
EnglishSpecialized interop skill for deliberate experiments with native Qiskit Machine Learning abstractions.
EnglishDiagnostic skill for identifying and categorizing failures in PennyLane plus PyTorch QML workflows.
EnglishGrounding skill for framing quantum machine learning problems before implementation.
EnglishResearch translation skill for turning QML papers into concrete implementation and evaluation plans.
EnglishPyTorch-facing interface skill for clean tensor, parameter, and prediction boundaries around PennyLane models.
EnglishMeta-skill that routes ambiguous PennyLane plus PyTorch QML requests to the correct implementation skill.
EnglishReproducibility skill for making PennyLane plus PyTorch QML experiments rerunnable, comparable, and defensible.
EnglishEvaluation skill for fair comparisons across PennyLane plus PyTorch, Qiskit-backed, and native Qiskit ML branches.
Uncertain
AGENT SKILLS ATLASIncludes Pennylane Qiskit Backends, Pennylane Qnn, Qiskit Machine Learning Interop, and other workflows.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLISupported by the Atlas install prompt. Skill-specific requirements vary.
Pack stats
What’s included
Pennylane Qiskit Backends
Backend-extension skill for running PennyLane workflows on Qiskit simulators and IBM-compatible paths.
Pennylane Qnn
Core implementation skill for PennyLane-first hybrid quantum neural network models.
Qiskit Machine Learning Interop
Specialized interop skill for deliberate experiments with native Qiskit Machine Learning abstractions.
Qml Debugging
Diagnostic skill for identifying and categorizing failures in PennyLane plus PyTorch QML workflows.
Qml Foundations
Grounding skill for framing quantum machine learning problems before implementation.
Qml Paper Replication
Research translation skill for turning QML papers into concrete implementation and evaluation plans.
Qml PyTorch Interface
PyTorch-facing interface skill for clean tensor, parameter, and prediction boundaries around PennyLane models.
Qml PyTorch Router
Meta-skill that routes ambiguous PennyLane plus PyTorch QML requests to the correct implementation skill.
Qml Reproducibility
Reproducibility skill for making PennyLane plus PyTorch QML experiments rerunnable, comparable, and defensible.
Qml Cross Framework Benchmarking
Evaluation skill for fair comparisons across PennyLane plus PyTorch, Qiskit-backed, and native Qiskit ML branches.
Quick start
- Choose your skills. Select individual skills above, or choose the entire pack.
- Copy the install prompt. Use Copy install prompt above the list.
- Send it to your agent. Paste into your coding agent’s chat. Your agent reviews the source and guides the setup.
Try it with your agent
After installation, start with a request like:
Use the Pennylane Qiskit Backends skill to help with my task.
Ask me for the context you need before getting started.Requirements & license
- A coding agent that can access your project files and install skills.
- Your agent checks each skill’s instructions, dependencies, and permissions during setup.
- Reported license for the skills shown: MIT. The author’s terms apply.