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

86%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

72%

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

The body is highly actionable and well-structured with clean progressive disclosure to verified reference files, but it loses conciseness to inline version/date specifics and duplicated topic lists, and its hardware workflows omit explicit validation feedback loops for costly operations.

Suggestions

Move time-sensitive version pins and dates (e.g., 'cirq==1.6.1', 'August 2025', Python 3.11+) into a dedicated 'Versioning / old patterns' section so the main Installation and Quick Start flow stays lean and date-agnostic.

Trim or remove the 'Common topics' bullet lists under each reference section, since they duplicate content already covered in the linked reference files; rely on the link plus a one-line summary instead.

Add explicit validation checkpoints and a validate→fix→retry loop to the hardware execution workflow (e.g., validate the circuit against device constraints before submitting, and handle job failures with a retry path), given these are costly and hard to reverse operations.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and avoids explaining concepts Claude already knows, but inline time-sensitive specifics (version pins like 'cirq==1.6.1', 'August 2025', Python 3.11+) sit in the main flow rather than a deprecated/old-patterns section, and the per-section 'Common topics' lists partly duplicate the reference files.	2 / 3
Actionability	Provides complete, executable, copy-paste-ready code (basic and parameterized circuits, variational and hardware-execution templates, noise study) plus exact pinned install commands — concrete rather than abstract.	3 / 3
Workflow Clarity	Steps are clearly listed and sequenced (build → simulate → optimize → run; Best Practices per phase) and a troubleshooting section exists, but the costly/hard-to-reverse hardware workflows lack explicit validation checkpoints and validate→fix→retry feedback loops, capping this dimension at 2.	2 / 3
Progressive Disclosure	Body is a clear overview that links to six real, one-level-deep reference files (building, simulation, transformation, hardware, noise, experiments), each signaled with a markdown link and a short topic preview; content is appropriately split with easy navigation.	3 / 3
	Total	10 / 12 Passed

Description

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.

The description is specific, complete, and distinctive: it states concrete capabilities, provides explicit 'Use when' triggers, and disambiguates against the most likely competing skills. It is concise without fluff or over-claims.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions — 'targeting Google Quantum AI hardware', 'designing noise-aware circuits', 'running quantum characterization experiments', 'noise modeling', and 'low-level circuit design' — rather than vague domain language.	3 / 3
Completeness	Explicitly answers what ('Google quantum computing framework') and when via a clear 'Use when...' clause spanning hardware targeting, noise-aware circuits, and characterization experiments.	3 / 3
Trigger Term Quality	Covers natural terms a quantum user would say (Google hardware, noise-aware circuits, characterization, IBM hardware, quantum ML, physics simulations); these are the phrasings users reach for when they need this skill.	3 / 3
Distinctiveness Conflict Risk	Carves a clear Google-hardware niche and explicitly routes away from likely rivals ('For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip'), minimizing wrong-skill triggering.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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: 757b63b

Reviewed: about 11 hours ago

Table of Contents

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