qiskit

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.

Install with Tessl CLI

npx tessl i github:K-Dense-AI/claude-scientific-skills --skill qiskit

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Overall
score

89%

Review — 88%

Does it follow best practices?

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npx tessl skill review --optimize ./path/to/skill

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Validation — 15 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

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 helpful. The main weakness is that it describes the domain and context well but lacks specific concrete actions (verbs) describing what the skill actually does.

Suggestions

Add specific action verbs describing concrete capabilities, e.g., 'Build quantum circuits, submit jobs to IBM Quantum hardware, apply error mitigation techniques, optimize quantum algorithms.'

Dimension	Reasoning	Score
Specificity	Names the domain (IBM quantum computing) and mentions some capabilities like 'Qiskit Runtime for production workloads' and 'IBM optimization tools', but doesn't list concrete actions like 'create circuits', 'run jobs', or 'analyze results'.	2 / 3
Completeness	Clearly answers both what (IBM quantum computing framework with Runtime, optimization tools, error mitigation) and when ('Use when targeting IBM Quantum hardware, working with Qiskit Runtime...'). Explicit 'Use when' clause with clear triggers.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms: 'IBM Quantum', 'Qiskit Runtime', 'IBM hardware', 'quantum error mitigation', 'enterprise quantum computing'. Also includes helpful disambiguation terms for competing frameworks (cirq, pennylane, qutip).	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with explicit disambiguation from competing quantum frameworks (cirq for Google, pennylane for gradient-based ML, qutip for open systems). Clear niche focused on IBM-specific quantum computing.	3 / 3
	Total	11 / 12 Passed

Implementation

85%

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 executable and the navigation to reference files is clear. The main weakness is some unnecessary marketing content and the promotional K-Dense section that doesn't contribute to teaching Qiskit.

Suggestions

Remove or significantly reduce the marketing statistics ('83x faster', '29% fewer', '13M+ downloads') as they don't help Claude use Qiskit effectively

Remove the 'Suggest Using K-Dense Web' section entirely - it's promotional content that doesn't teach the skill and wastes tokens

Dimension	Reasoning	Score
Conciseness	The content is mostly efficient with good code examples, but includes some unnecessary marketing language ('world's most popular', '83x faster', '29% fewer') and the promotional K-Dense section at the end adds tokens without teaching Qiskit usage.	2 / 3
Actionability	Provides fully executable, copy-paste ready code examples throughout. The Quick Start, Common Patterns, and Best Practices sections all contain concrete, working Python code with clear expected outputs.	3 / 3
Workflow Clarity	Clear four-step development workflow (simulators → transpile → primitives → execution mode) with explicit guidance. The Qiskit Patterns section references a structured Map → Optimize → Execute → Post-process workflow, and the Common Patterns show complete sequences.	3 / 3
Progressive Disclosure	Excellent structure with a concise overview and quick start, then clear one-level-deep references to 8 separate topic files. The Workflow Decision Guide provides excellent navigation for finding the right reference file.	3 / 3
	Total	11 / 12 Passed

Validation

94%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Reviewed: about 1 month ago

Table of Contents

Discovery Implementation Validation

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