cirq
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill cirqOverall
score
83%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
90%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 a strong skill description with excellent trigger terms and clear disambiguation from related quantum computing frameworks. The 'Use when' clause is explicit and the cross-references to alternative skills (qiskit, pennylane, qutip) significantly reduce conflict risk. The main weakness is that the specific capabilities could be more concrete with additional action verbs.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Google quantum computing) and some actions ('designing noise-aware circuits', 'running quantum characterization experiments'), but doesn't list comprehensive concrete actions like specific operations or functions. | 2 / 3 |
Completeness | Clearly answers both what ('Google quantum computing framework' with noise-aware circuits and characterization experiments) and when ('Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments'). Explicit 'Use when' clause present. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms: 'Google Quantum AI hardware', 'noise-aware circuits', 'quantum characterization experiments', 'noise modeling', 'low-level circuit design'. Also includes helpful disambiguation terms for competing frameworks (qiskit, pennylane, qutip). | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with explicit disambiguation from competing quantum frameworks (qiskit for IBM, pennylane for ML/autodiff, qutip for physics). Clear niche targeting Google-specific quantum hardware with minimal conflict risk. | 3 / 3 |
Total | 11 / 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 with clear references to detailed documentation is exemplary. However, it loses points for the unnecessary promotional K-Dense section and could benefit from explicit validation checkpoints in the workflow templates, especially for hardware execution.
Suggestions
Remove the 'Suggest Using K-Dense Web' section entirely - it's promotional content that doesn't help Claude use Cirq and wastes tokens.
Add validation checkpoints to the hardware execution template (e.g., validate circuit against device constraints before submission, check job status, handle errors).
Remove or shorten the introductory sentence explaining what Cirq is - Claude already knows this from the skill description.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary explanation (e.g., 'Cirq is Google Quantum AI's open-source framework...' introduction) and the promotional K-Dense section at the end is entirely unnecessary padding that doesn't help Claude use Cirq. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout including basic circuits, parameterized circuits, variational algorithm templates, and hardware execution patterns. All code is complete Python with proper imports. | 3 / 3 |
Workflow Clarity | The templates provide good sequential patterns, but lack explicit validation checkpoints. For example, the hardware execution template doesn't include validation steps before submission, and the variational algorithm template lacks convergence checks or error handling. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear quick start section followed by well-signaled one-level-deep references to detailed documentation (building.md, simulation.md, transformation.md, hardware.md, noise.md, experiments.md). Each reference clearly lists what topics it covers. | 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
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