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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.

64

Quality

81%

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SKILL.md
Quality
Evals
Security

Quality

Content

65%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured framework overview with executable examples and exemplary progressive disclosure into real reference files. Its weaknesses are redundancy — the reference index appears twice — and workflows that demonstrate sequencing without any validation or convergence checkpoints.

Suggestions

Merge the 'Core Capabilities' bullets and the 'Detailed Documentation' section into a single reference index so each file is listed once with its topic summary.

Add validation checkpoints to the workflows: print or assert loss convergence in the classifier loop, and compare the VQE energy against the Hartree-Fock reference value.

Make code examples self-contained by defining 'dev', 'X_train'/'y_train', and 'circuit_function' (or explicitly noting they come from earlier examples) so each snippet runs as written.

DimensionReasoningScore

Conciseness

The body is mostly efficient, but the Core Capabilities bullet lists and the 'Detailed Documentation' section index the same seven reference files twice with overlapping topic summaries, and the Overview paragraph restates the frontmatter description. This matches the level-3 anchor ('some unnecessary explanation or could be tightened'); level 4 would require only minor trims, whereas here an entire redundant section could be removed.

3 / 5

Actionability

Install commands and code examples are largely executable — the quick-start qnode/optimizer loop, the VQE workflow with qchem.molecular_hamiltonian, and concrete best practices ('Use parameter-shift for hardware', 'qml.specs()'). Minor gaps keep it below level 5: the classifier example references undefined 'dev', 'X_train', 'y_train', the device-switching snippet uses an undefined 'circuit_function', and the quick-start circuit is built but never evaluated.

4 / 5

Workflow Clarity

The three Common Workflows carry clear step comments ('# 1. Build Hamiltonian', '# 2. Define ansatz', '# 3. Optimize') so the sequences are coherent, but no validation checkpoints exist anywhere — training loops lack convergence checks and the VQE example never verifies the energy against a reference like Hartree-Fock. This matches the level-3 anchor ('sequence present but checkpoints missing or implicit') rather than level 4, which requires most checkpoints present.

3 / 5

Progressive Disclosure

The body is a clean overview with inline quick-start material and seven well-signaled one-level-deep references ('See references/quantum_circuits.md for: …'), all of which exist on disk, plus a consolidated index and resources section. This matches the level-5 anchor; the duplicated reference listing aids navigation rather than burying it, and nothing nests two levels deep.

5 / 5

Total

15

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An excellent description that answers what the skill does, when to use it, and which neighboring skills to prefer instead, all in concrete terms. The only weakness is that the trigger vocabulary omits the product name 'PennyLane' and a few spelled-out synonyms, relying on abbreviated forms.

Suggestions

Mention 'PennyLane' by name in the description so users who ask for it verbatim get a direct keyword match.

Spell out 'quantum machine learning' and 'variational quantum eigensolver' at least once alongside the VQE/QAOA abbreviations to widen the natural-phrase coverage.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'training quantum circuits via gradients', 'building hybrid quantum-classical models', 'needing device portability across IBM/Google/Rigetti/IonQ', and 'integration with PyTorch/JAX/TensorFlow' — giving comprehensive coverage of the framework's capabilities. It clearly matches the level-5 anchor; level 4 would require minor coverage gaps, and none are evident.

5 / 5

Completeness

Both halves are explicit: the 'what' is stated concretely ('Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow') and the 'when' opens with a literal 'Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability'. This matches the level-5 anchor; level 4 would require a vaguer or less explicit 'when' clause.

5 / 5

Trigger Term Quality

Natural trigger terms are strong — 'VQE, QAOA', 'quantum neural networks', 'hybrid quantum-classical models', 'PyTorch/JAX/TensorFlow', 'qiskit', 'cirq', 'qutip' are phrases users would actually say. However the library's own name 'PennyLane' and spelled-out synonyms like 'quantum machine learning' or 'variational quantum eigensolver' never appear, leaving a few natural terms missing — the level-4 anchor rather than the synonym-complete level 5.

4 / 5

Distinctiveness Conflict Risk

The description ends with explicit de-confliction guidance — 'For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip' — which carves out a clear niche with minimal overlap risk. This is stronger than the level-4 anchor, which only requires mostly distinct scope without active boundary steering.

5 / 5

Total

19

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/quantum/pennylane/SKILL.md

Reviewed

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