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.

Quality

80%

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Quality

Content

72%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, actionable skill with excellent progressive disclosure and executable examples. Weakest in conciseness (duplicated topic lists) and workflow clarity (no explicit validation checkpoints for costly hardware runs).

Suggestions

Remove the 'Common topics' bullet lists under each Core Capabilities section, or move them into the referenced files, to cut duplication and improve conciseness.

Add an explicit validation checkpoint in the Hardware Execution Template (e.g. validate the circuit against device.constraints and abort/retry on failure before submission) to support a workflow_clarity score above 3.

Add a short validate-then-retry loop in the Noise Study / optimization templates so error recovery is explicit rather than implied.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with executable Quick Start and templates, but each Core Capabilities section repeats a 'Common topics' bullet list that duplicates the referenced file contents, adding padding that could be trimmed.	3 / 5
Actionability	Provides copy-paste-ready install commands, runnable Quick Start circuits, parameterized sweeps, and full hardware/noise templates covering the common cases.	5 / 5
Workflow Clarity	Multi-step hardware and optimization templates are sequenced, but expensive/destructive hardware runs lack explicit validate-checkpoint / error-recovery feedback loops (e.g. validate against device constraints before submission), which caps this at 3.	3 / 5
Progressive Disclosure	SKILL.md is a concise overview with well-signaled, one-level-deep references to real files (building.md, simulation.md, transformation.md, hardware.md, noise.md, experiments.md), making navigation easy.	5 / 5
	Total	16 / 20 Passed

Description

87%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.

A strong description that pairs concrete Google-quantum capabilities with explicit 'Use when' triggers and clear tool-routing for adjacent domains. Minor room to add more enumerated actions and common synonyms.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('designing noise-aware circuits, or running quantum characterization experiments', 'noise modeling, and low-level circuit design') with minor coverage gaps; not as comprehensive as listing all capabilities, so a 4 rather than 5.	4 / 5
Completeness	Explicitly answers both 'what' (Google quantum computing framework, noise modeling, low-level circuit design) and 'when' via a concrete 'Use when targeting...' clause with multiple trigger conditions.	5 / 5
Trigger Term Quality	Includes natural phrases a user would say ('targeting Google Quantum AI hardware', 'noise-aware circuits', 'quantum characterization experiments') and names alternative tools, but lacks synonyms/file extensions; a few natural terms missing.	4 / 5
Distinctiveness Conflict Risk	Clear niche (Google Quantum AI hardware) with explicit routing to qiskit/pennylane/qutip for other use cases, giving minimal conflict risk.	5 / 5
	Total	18 / 20 Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/cirq/SKILL.md
Commit: b2a92ba

Reviewed: 3 days ago

Table of Contents

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