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

78%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

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tessl review fix ./backend/cli/skills/quantum/cirq/SKILL.md

Quality

Content

72%

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 body with strong progressive disclosure and executable examples. The main weaknesses are mild verbosity from duplicated 'Common topics' previews and the absence of explicit validation checkpoints in the hardware-execution and circuit-transformation templates.

Suggestions

Trim or consolidate the 'Common topics' bullet lists that merely preview the linked reference files, keeping only genuinely load-bearing signposts.

Add explicit validation checkpoints (e.g., validate-against-device-constraints -> fix -> re-validate) inside the run_on_hardware and circuit-transformation templates rather than only in Best Practices.

Drop the redundant opening sentence that restates the frontmatter description.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with lean, executable code, but the per-section 'Common topics' bullet lists (≈7 bullets × 6 sections) preview content already in the reference files, and the opening sentence restates the frontmatter description; could be tightened.	2 / 3
Actionability	Provides fully executable copy-paste code in Quick Start and a parameterized-circuit example, plus three complete templates (variational algorithm, hardware execution, noise study) and concrete install commands.	3 / 3
Workflow Clarity	Best Practices mentions validation ('Validate circuits against device constraints', 'Validate correctness after transformation') but the hardware-execution and transformation templates lack explicit checkpointed validate->fix->retry steps; per the rubric, missing validation for destructive/batch operations caps this at 2.	2 / 3
Progressive Disclosure	SKILL.md is a concise overview pointing to six real, one-level-deep reference files (building.md, simulation.md, transformation.md, hardware.md, noise.md, experiments.md), each clearly signaled with a markdown link for easy navigation.	3 / 3
	Total	10 / 12 Passed

Description

85%

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 clearly states the framework's purpose, provides an explicit 'Use when' trigger, and proactively disambiguates against competing quantum libraries. It would benefit from adding the bare product name 'cirq' and the generic phrase 'quantum circuits' as trigger terms.

Suggestions

Add the bare keyword 'cirq' and the generic phrase 'quantum circuits' to the trigger clause so users who say those exact words match this skill.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions: 'designing noise-aware circuits', 'running quantum characterization experiments', 'noise modeling', and 'low-level circuit design', matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers both what ('Google quantum computing framework... noise modeling, low-level circuit design') and when ('Use when targeting Google Quantum AI hardware, designing noise-aware circuits...'), with an explicit 'Use when' trigger.	3 / 3
Trigger Term Quality	Includes relevant natural terms like 'Google Quantum AI hardware' and 'noise-aware circuits', but misses common variations such as the bare keyword 'cirq' or the generic phrase 'quantum circuits' a user might say.	2 / 3
Distinctiveness Conflict Risk	Clear niche with explicit routing to avoid conflicts ('For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip'), making it unlikely to trigger for the wrong skill.	3 / 3
	Total	11 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
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/cirq/SKILL.md
Commit: 52845c3

Reviewed: about 9 hours ago

Table of Contents

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