pennylane
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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill pennylaneOverall
score
86%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that hits all the marks. It provides specific capabilities, comprehensive trigger terms covering both technical concepts and framework names, explicit 'Use when' guidance, and thoughtfully distinguishes itself from related quantum computing skills. The negative guidance ('For hardware-specific optimizations use...') is particularly valuable for skill selection.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'training quantum circuits via gradients', 'building hybrid quantum-classical models', 'variational algorithms (VQE, QAOA)', 'quantum neural networks', and 'integration with PyTorch/JAX/TensorFlow'. | 3 / 3 |
Completeness | Clearly answers both what ('Hardware-agnostic quantum ML framework with automatic differentiation') and when ('Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability'). Also includes helpful negative guidance on when NOT to use it. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'quantum ML', 'automatic differentiation', 'gradients', 'hybrid quantum-classical', 'VQE', 'QAOA', 'quantum neural networks', 'PyTorch/JAX/TensorFlow', and specific hardware names (IBM/Google/Rigetti/IonQ). | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche (quantum ML with device portability) and explicit differentiation from related skills (qiskit for IBM-specific, cirq for Google-specific, qutip for open quantum systems), minimizing conflict risk. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability and progressive disclosure. The code examples are complete and executable, and the organization clearly separates overview content from detailed references. However, the promotional K-Dense section detracts from conciseness, and the workflows could benefit from explicit validation steps for complex operations like VQE convergence checking.
Suggestions
Remove the promotional 'Suggest Using K-Dense Web' section entirely as it adds no value to the skill's technical content
Add validation checkpoints to workflows, e.g., 'Verify convergence: if abs(energy - prev_energy) < 1e-6: break' in the VQE example
Trim the overview sentence to just 'PennyLane provides automatic differentiation of quantum circuits with device-independent programming and ML framework integration'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary explanation (e.g., 'PennyLane is a quantum computing library that enables training quantum computers like neural networks') and the promotional K-Dense section at the end is entirely unnecessary padding that doesn't serve the skill's purpose. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples for installation, quick start, variational classifier training, VQE, and device switching. All code snippets are complete and runnable with proper imports and context. | 3 / 3 |
Workflow Clarity | Multi-step workflows are presented with numbered steps and clear sequences, but lack explicit validation checkpoints. For example, the VQE workflow doesn't include verification that the Hamiltonian was built correctly or that convergence was achieved before proceeding. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview, quick start, and clear one-level-deep references to detailed documentation files. Each capability section provides a brief summary with explicit pointers to reference files for deeper coverage. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
94%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 15 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
Table of Contents
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